Site built with pkgdown 2.0.9.
+Site built with pkgdown 2.1.1.
- + - - +Step-by-Step Execution of the pathfindR Enrichment Workflow
Ege Ulgen
-2024-05-04
+2024-10-14
- Source:vignettes/manual_execution.Rmd
+ Source: vignettes/manual_execution.Rmd
manual_execution.RmdVisualizations - -
Visualizations
-Site built with pkgdown 2.0.9.
+Site built with pkgdown 2.1.1.
-
+
-
-
+
-
+
@@ -96,7 +95,7 @@
-
+
@@ -105,9 +104,9 @@ pathfindR Analysis for non-Homo-sapiens
organisms
Ege Ulgen
- 2024-05-04
+ 2024-10-14
- Source: vignettes/non_hs_analysis.Rmd
+ Source: vignettes/non_hs_analysis.Rmd
non_hs_analysis.Rmd
@@ -844,9 +843,7 @@ Built-in Mus musculus Data
-
-
+
@@ -859,17 +856,17 @@ Built-in Mus musculus Data
-Site built with pkgdown 2.0.9.
+Site built with pkgdown 2.1.1.
-
+
-
-
+
-
+
@@ -96,16 +95,16 @@
-
+
Obtaining PIN and Gene Sets Data
- 2024-05-04
+ 2024-10-14
- Source: vignettes/obtain_data.Rmd
+ Source: vignettes/obtain_data.Rmd
obtain_data.Rmd
@@ -230,9 +229,7 @@ MSigDB Gene Sets
-
-
+
@@ -245,17 +242,17 @@ MSigDB Gene Sets
-Site built with pkgdown 2.0.9.
+Site built with pkgdown 2.1.1.
-
+
-
-
+
-
+
@@ -96,7 +95,7 @@
-
+
@@ -104,9 +103,9 @@
Visualization of pathfindR Enrichment
Results
- 2024-05-04
+ 2024-10-14
- Source: vignettes/visualization_vignette.Rmd
+ Source: vignettes/visualization_vignette.Rmd
visualization_vignette.Rmd
@@ -253,8 +252,9 @@
By default the node sizes are plotted proportional to the number of
genes a term contains (num_genes). To adjust node sizes
-using the \(-log_{10}\)(lowest p
-values), set node_size = "p_val":
+using the
+(lowest
+p values), set node_size = "p_val":
term_gene_graph(example_pathfindR_output, num_terms = 3, node_size = "p_val")
See ?term_gene_graph for more details.
@@ -300,9 +300,7 @@
+
@@ -315,17 +313,17 @@
-
+
-
+
-
+
@@ -77,7 +77,7 @@
Authors and Citation
-
+
-
+
Changelog • pathfindR
-
-
+
+
-
+
-
+
- - Version dev
- - Version 2.4
- - Version 2.3
- - Version 2.2
- - Version 2.1
- - Version 2.0
- - Version 1.6
- - Version 1.5
- - Version 1.4
- - Version 1.3
- - Version 1.2
- - Version 1.1
-
+
+
+
+pathfindR 2.4.12024-05-04
+
+Minor Changes and Bug Fixes
+- fixed a bug regarding KEGG gene set fetching: removed the conversion functionality in
get_kegg_gsets() which now returns KEGG IDs so that the user can convert the returned identifiers using a more appropriate tool (e.g. BioMart) should they wish
+
+
+
+pathfindR 2.4.02024-04-28
+
+Major Changes
+- implemented a new
color_kegg_pathway() function using ggkegg to create colored KEGG pathway ggplot objects (instead of using KEGGREST to obtain the colored PNG files, which no longer works #169)
+- renamed the
visualize_hsa_KEGG function to visualize_KEGG_diagram() to reflect this is now able to handle KEGG pathway enrichment results from any organism
+- updated the
visualize_terms(), visualize_term_interactions() and visualize_KEGG_diagram() functions so that they now return a list of ggplot objects (named by term ID)
+- updated the
get_kegg_gsets() function to also use ggkegg for fetching genes per pathway data
+- removed unneeded dependencies:
magick, KEGGgraph and KEGGREST
+
+
+
+Minor Changes and Bug Fixes
+- updated the
get_biogrid_pin() function so that it can now determine the latest version and download/process it from BioGRID (via setting release = "latest", which is now the default behavior)
+
+
+
+pathfindR 2.3.12024-01-19
+
+Minor Changes and Bug Fixes
+- fixed a bug in the
UpSet_plot() plot function regarding the interaction with ggupset package that was discovered in a reverse dependency check for ggplot2 3.5.0 (#189)
+- fixed gene symbol case mismatch issue in
score_terms() (#186)
+- applied enhancement suggestion from #184 to enable scale fill manual for
term_gene_graph()
+
+
+
+
+pathfindR 2.3.02023-10-08
+
+Major Changes
+- reverted removal of
create_HTML_report() so run_pathfindR() once again generates HTML reports
+
+
+
+pathfindR 2.2.02023-08-20
+
+Minor Changes and Bug Fixes
+- added the
disable_parallel argument in active_snw_enrichment_wrapper() to be able to disable parallel runs via foreach
+
+- fixed the issue encountered on CentOS where
forech wasn’t loading pathfindR (#164)
+- fixed a CRAN error due to a package documentation issue (#172)
+- performed some refactoring and updated/improved all tests
+
+
+
+pathfindR 2.1.02023-05-12
+
+Minor Changes and Bug Fixes
+- removed
create_HTML_report() so run_pathfindR() no longer generates a HTML report
+
+
+
+pathfindR 2.0.12023-05-10
+
+Minor Changes and Bug Fixes
+- added the
dir_for_report argument in the internal function create_HTML_report() to fix test issues on CRAN
+
+
+
+pathfindR 2.0.02023-05-06
+
+Major Changes
+- updated the java active subnetwork search component and added the
seedForRandom argument in active_snw_search()to ensure reproducibility. By default behavior, in run_pathfindR(), a seed is set for each iteration to produce reproducible results (#108)
+- as the example input/output data were renamed for convenience in ‘pathfindR.data’ v2.0, ‘pathfindR’ now depends on pathfindR.data (>= 2.0)
+- refactored/simplified
run_pathfindR()
+
+- visualization enriched term diagrams are now NOT part of
run_pathfindR()
+
+- default behavior of
run_pathfindR() is now to run in a temporary directory. The user can still set output_dir to run in a specified directory and also produce HTML reports
+- in
hierarchical_term_clustering(), update the sequence of number of clusters for which silhouette width is calculated for choosing the optimal number of clusters. This should speed up the function for cases with a large number of enriched terms
+- updated the relevant vignettes to reflect the implemented changes
+
+
+Minor Changes and Bug Fixes
+- fixed a minor issue in
return_pin_path() where the PIN was not properly read (#157)
+
+
+
+pathfindR 1.6.42022-08-25
+
+Minor Changes and Bug Fixes
+- updated the alias selection function within
input_processing() so that an alias that is not already present is selected
+- updated the min-max scaling (controlled by
scale_vals) in color_kegg_pathway(), the default is now scale_vals=TRUE
+
+- updated the
term_gene_heatmap() function so that legend title is shown and can be customized
+- updated the
term_gene_heatmap() function so that coloring is proper when no change values are provided in genes_df
+
+- added the
sort_terms_by_p argument to the term_gene_heatmap() function to enable sorting of terms by ‘lowest_p’
+- in visualization functions, made coloring of up-/down-regulated genes consistent (#126)
+- added the
vertex.label.cex and vertex.size.scaling arguments to cluster_graph_vis()
+
+- added the
show_legend argument to visualize_term_interactions() to toggle the legend
+
+
+
+pathfindR 1.6.32021-11-15
+
+Minor Changes and Bug Fixes
+- Fixed coloring issue in
color_kegg_pathway()
+
+- In
color_kegg_pathway() the default value for normalize_vals is now FALSE
+
+
+
+
+pathfindR 1.6.22021-08-21
+
+Major Changes
+- fixed an issue in
get_kegg_gsets() where empty result was returned for some organisms due to an error in parsing (#72)
+
+
+Minor Changes and Bug Fixes
+- added
repel = TRUE in term_gene_graph() and combined_results_graph() for better visualization of labels
+- fixed minor issue in
enrichment_chart() (#75)
+- fixed minor issue in
visualize_term_interactions()
+
+- fixed issue in
get_biogrid_pin() where the download method was set to wget (now set to auto, per #83)
+- updated to using tab3 format for
get_biogrid_pin() (if tab3 is available for the chosen release, otherwise tab2 format is used)
+- updated the default version of PIN obtained by
get_biogrid_pin() to ‘4.4.200’
+- in
get_kegg_gsets(), improved parsing of KEGG term descriptions so that no description is duplicated (#87)
+- in
score_terms(), if using descriptions, the ID is now appended for (any) duplicated term descriptions (#87)
+- in
obtain_colored_url(), swapped bg_color with fg_color due to an issue with KEGGREST
+
+- added legend to
term_gene_heatmap() (#95)
+- in
get_biogrid_pin(), the “download.file.method” from global options is used
+-
+
combined_results_graph() raises an error if there are no common terms in the combined data frame
+
+
+
+pathfindR 1.6.12021-01-04
+
+Major Changes
+- In
run_pathfindR(), the default iterations was set back to 10 (the default for all other v1.x)
+
+
+pathfindR 1.6.02020-11-21
+
+Major Changes
+- In
run_pathfindR(), as “GR” (the default active subnetwork search method) provides nearly identical results in each iteration, the default iterations is set to 1
+- added the column ‘support’ (the proportion of active subnetworks leading to enrichment over all subnetworks) in the output
+- updated the download URL in
get_biogrid_pin() as BioGRID updated the URL for download
+
+
+Minor Changes and Bug Fixes
+- changed old argument in the “Step-by-Step Execution of the pathfindR Enrichment Workflow” vignette
+- fixed an issue in
visualize_term_interactions() where the file name was too long, it was causing an error on Windows. Limited to 100 characters (#58)
+
+
+
+pathfindR 1.5.12020-09-20
+
+Minor Changes and Bug Fixes
+- Fixed issue in
check_java_version() where java version 14 could not be parsed (#49)
+- Fixed issue in
combined_results_graph() where gene nodes were not colored correctly (#55)
+
+
+
+pathfindR 1.5.02020-06-06
+
+Major Changes
+- created separate package
pathfindR.data for storing pathfindR data
+- added the function
visualize_active_subnetworks() for visualizing graphs of active subnetworks
+- add the new vignette “Comparing Two pathfindR Results” that briefly describes how different pathfindR results can be compared
+- added the functions
combine_pathfindR_results() and combined_results_graph() for comparison of 2 pathfindR results and term-gene graph of the combined results, respectively
+- added the function
get_pin_file() for obtaining organism-specific PIN data (only from BioGRID for now)
+- added the function
get_gene_sets_list() for obtaining organism-specific gene sets list from KEGG, Reactome and MSigDB
+- added the function
term_gene_heatmap() to create heatmap visualizations of enriched terms and the involved input genes. Rows are enriched terms and columns are involved input genes. If genes_df is provided, colors of the tiles indicate the change values
+- added the function
UpSet_plot() to create UpSet plots of enriched terms
+- added the human cell markers gene sets data
cell_markers_gsets and cell_markers_descriptions
+
+
+
+Minor Changes and Bug Fixes
+- fixed an issue regarding
parallel::makeCluster() in run_pathfindR() (#45)
+- fixed save-related issue in
download_kegg_png() (#37, @rix133)
+- added the output data
RA_comparison_output of pathfindR results on another RA-related dataset (GSE84074)
+- in
visualize_hsa_KEGG(), fixed the issue where >1 entrez ids were returned for a gene symbol (the first one is kept)
+- in
visualize_hsa_KEGG(), implemented a tryCatch to avoid any issues when KEGGREST::color.pathway.by.objects() might fail (#28)
+- in
visualize_hsa_KEGG(), now limiting the number of genes passes onto KEGGREST::color.pathway.by.objects() to < 60 (because the KEGG API now limits the number?)
+- changed default visualization in
term_gene_heatmap() (i.e. when genes_df is not provided) to binary colored heatmap (by default, “green” and “red”, controlled by low and high) by up-/down- regulation status
+- update the vignette “pathfindR Analysis for non-Homo-sapiens organisms” to reflect new data generation functions
get_pin_file() and get_gene_sets_list() and fixed a minor issue in the vignette (#46)
+
+
+
+pathfindR 1.4.22019-12-06
+
+Minor Changes and Bug Fixes
+- Fixed corner case in
create_kappa_matrix() when chance is 1, the metric is turned into 0
+- Fixed misused
class(.) == * in cluster_graph_vis()
+
+
+
+
+pathfindR 1.4.12019-11-15
+
+Major Changes
+- Fixed error in DESCRIPTION: the Java version in SystemRequirements was corrected to “Java (>= 8.0)”
+- The Java version is now checked
+
+
+Minor Changes and Bug Fixes
+- Fixed behavior: when no input genes are present in the enriched hsa KEGG pathway, visualization of the pathway is now skipped
+- Added the argument
max_to_plot to visualize_hsa_KEGG() and to run_pathfindR(). This argument controls the number of pathways to be visualized (default is NULL, i.e. no filter). This was implemented not to slow down the runtime of run_pathfindR() as downloading the png files is slow.
+- Fixed links to visualizations in
enriched_ters.Rmd
+
+
+
+
+pathfindR 1.4.02019-11-08
+
+Major Changes
+- Replaced most occurrences of “pathway” to “term”. This was adapted because “term” reflects the utility of the package better. The enrichment and clustering approaches work with any kind of gene set data (be it pathway gene sets, gene ontology gene sets, motif gene sets etc.) Accordingly:
+
-
+
DESCRIPTION was updated
+- The functions
annotate_pathway_DEGs(), calculate_pw_scores(), cluster_pathways(), fuzzy_pw_clustering(), hierarchical_pw_clustering(), visualize_pw_interactions() and visualize_pws() were renamed to annotate_term_DEGs(), score_terms(), cluster_enriched_terms(), fuzzy_term_clustering(), hierarchical_term_clustering(), visualize_term_interactions() and visualize_terms() respectively
+- The Rmd template file for the report
enriched_pathways.Rmd was renamed to enriched_terms.Rmd
+
+- All the Rmd template files for the report were updated
+- Documentation of each function was updated accordingly
+
+- Added the visualization function
term_gene_graph(), which creates a graph of enriched terms - involved genes
+- Made changes in
enrichment() and enrichment_analyses() to get enrichment results faster
+- Added the function
fetch_gene_set() for obtaining gene set data more easily
+- Terms in gene sets can now be filtered according to the number of genes a term contains (controlled by
min_gset_size, max_gset_size in fetch_gene_set() and run_pathfindR())
+- Added the argument
gaCrossover during active subnetwork search which controls the probability of a crossover in GA (default = 1, i.e. always perform crossover)
+- Added unit tests using
testthat
+
+- Updated all gene sets data
+- Updated all RA example data
+- The vignettes were updated
+- Updated all PIN data
+- Improved speed of kappa matrix calculation (
create_kappa_matrix())
+- Added vignette for non-Homo-sapiens organisms
+- Added Mus musculus (mmu) data:
+
-
+
mmu_kegg_genes & mmu_kegg_descriptions: mmu KEGG gene sets data
+- mmu STRING PIN
+-
+
myeloma_input & myeloma_output: example mmu input and output data
+
+- Added the STRING PIN (combined score >= 400)
+- The argument
sig_gene_thr in subnetwork filtering via filterActiveSnws() now serves the threshold proportion of significant genes in the active subnetwork. e.g., if there are 100 significant genes and sig_gene_thr = 0.03, subnetwork that contain at least 3 (100 x 0.03) significant genes will be accepted for further analysis
+- Removed
pathview dependency by implementing colored pathway diagram visualization function using KEGGREST and KEGGgraph
+
+
+
+Minor Changes and Bug Fixes
+- In
hierarchical_term_clustering(), redefined the distance measure as 1 - kappa statistic
+
+- Fixed minor issue in
cluster_graph_vis() (during the calculations for additional node colors)
+- Removed title from graph visualization of hierarchical clustering in
cluster_graph_vis()
+
+- In
active_snw_search(), unnecessary warnings during active subnetwork search were removed
+- Fixed minor issue in
enrichment_chart(), supplying fuzzy clustered results no longer raises an error
+- Added new checks in
input_testing() and input_processing() to ensure that both the initial input data frame and the processed input data frame for active subnetwork search contain at least 2 genes (to fix the corner case encountered in issue #17)
+- Fixed minor issue in
enrichment_chart(), ensuring that bubble sizes displayed in the legend (proportional to # of DEGs) are integers
+- In
enrichment_chart(), added the arguments num_bubbles (default is 4) to control number of bubbles displayed in the legend and even_breaks (default is TRUE) to indicate if even increments of breaks are required
+- Updated the logo
+- Minor fix in
term_gene_graph() (create the igraph object as an undirected graph for better auto layout)
+- Minor fix in
visualize_term_interactions(). The legend no longer displays “Non-input Active Snw. Genes” if they were not provided
+- The argument
human_genes in run_pathfindR() and input_processing() was renamed as convert2alias
+
+- The gene symbols in the input data frame, the PIN and the gene sets are now turned into uppercase (for obtaining the best overlap)
+- Added the argument
top_terms to enrichment_chart(), controlling the number top enriched terms to plot (default is 10)
+- Other minor bug/error fixes
+
+
+
+pathfindR 1.3.02018-11-20
+
+Major Changes
+- Separated the steps of the function
run_pathfindR into individual functions: active_snw_search, enrichment_analyses, summarize_enrichment_results, annotate_pathway_DEGs, visualize_pws.
+- renamed the function
pathmap as visualize_hsa_KEGG, updated the function to produce different visualizations for inputs with binary change values (ordered) and no change values (the input_processing function, assigns a change value of 100 to all).
+- Created new the visualization function
visualize_pw_interactions, which creates PNG files visualizing the interactions (in the selected PIN) of genes involved in the given pathways.
+- Added new vignette, describing the step-by-step execution of the pathfindR workflow
+- Changed clustering metric to kappa statistic, created the new clustering related functions
create_kappa_matrix, hierarchical_pw_clustering, fuzzy_pw_clustering and cluster_pathways.
+- Implemented the new function
cluster_graph_vis for visualizing graph diagrams of clustering results.
+
+
+Minor Changes and Bug Fixes
+- Fixed the bug where the arguments
score_quan_thr and sig_gene_thr for run_pathfindR were not being utilized.
+- in
run_pathfindR, added message at the end of run, reporting the number enriched pathways.
+- the function
run_pathfindR now creates a variable org_dir that is the “path/to/original/working/directory”. org_dir is used in multiple functions to return to the original working directory if anything fails. This changes the previous behavior where if a function stopped with an error the directory was changed to “..”, i.e. the parent directory. This change was adapted so that the user is returned to the original working directory if they supply a recursive output folder (output_dir, e.g. “./ALL_RESULTS/RESULT_A”).
+- in
input_processing, added the argument human_genes to only perform alias symbol conversion when human gene symbols are provided. - Updated the Rmd files used to create the report HTML files
+- Added the data for
GO-All, all annotations in the GO database (BP+MF+CC)
+- Updated the vignette
pathfindR - An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworks to reflect the new functionalities.
+
+
+
+pathfindR 1.2.32018-10-26
+
+Minor Changes and Bug Fixes
+- in the function
plot_scores, added the argument label_cases to indicate whether or not to label the cases in the pathway scoring heatmap plot. Also added the argument case_control_titles which allows the user to change the default “Case” and “Control” headers. Also added the arguments low and high used to change the low and high end colors of the scoring color gradient.
+- in the function
plot_scores, reversed the color gradient to match the coloring scheme used by pathview (i.e. red for positive values, green for negative values)
+- minor change in
parseActiveSnwSearch, replaced score_thr by score_quan_thr. This was done so that the scoring filter for active subnetworks could be performed based on the distribution of the current active subnetworks and not using a constant empirical score value threshold.
+- minor change in
parseActiveSnwSearch, increased sig_gene_thr from 2 to 10 as we observed in most of the cases, this resulted in faster runs with comparable results.
+- in
choose_clusters, added the argument p_val_threshold to be used as p value threshold for filtering the enriched pathways prior to clustering.
+
+
+
+
+pathfindR 1.2.12018-08-17
+
+Major Changes
+- Added the option to specify a custom gene set when using
run_pathfindR. For this, the gene_sets argument should be set to “Custom” and custom_genes and custom_pathways should be provided.
+
+
+Minor Changes and Bug Fixes
+- fixed minor bug in
calculate_pw_scores where if there was one DEG, subsetting the experiment matrix failed
+- added if condition to check if there were DEGs in
calculate_pw_scores. If there is none, the pathway is skipped.
+- in
calculate_pw_scores, if cases are provided, the pathways are reordered before plotting the heat map and returning the matrix according to their activity in cases. This way, “up” pathways are grouped together, same for “down” pathways.
+- in
calculate_pwd, if a pathway has perfect overlap with other pathways, change the correlation value with 1 instead of NA.
+- in
choose_clusters, if result_df has less than 3 pathways, do not perform clustering.
+-
+
run_pathfindR checks whether the output directory (output_dir) already exists and if it exists, now appends “(1)” to output_dir and displays a warning message. This was implemented to prevent writing over existing results.
+- in run
run_pathfindR, recursive creation for the output directory (output_dir) is now supported.
+- in run
run_pathfindR, if no pathways are found, the function returns an empty data frame instead of raising an error.
+
+
+
+pathfindR 1.2
+
+Major Changes
+Implemented the (per subject) pathway scoring function calculate_pw_scores and the function to plot the heatmap of pathway scores per subject plot_scores.
Added the auto parameter to choose_clusters. When auto == TRUE (default), the function chooses the optimal number of clusters k automatically, as the value which maximizes the average silhouette width. It then returns a data frame with the cluster assignments and the representative/member statuses of each pathway.
Added the Fold_Enrichment column to the resulting data frame of enrichment, and as a corollary to the resulting data frame of run_pathfindR.
Added the option bubble to plot a bubble chart displaying the enrichment results in run_pathfindR using the helper function enrichment_chart. To plot the bubble chart set bubble = TRUE in run_pathfindR or use enrichment_chart(your_result_df).
+
+Minor Changes and Bug Fixes
+Add the parameter silent_option to run_pathfindR. When silent_option == TRUE (default), the console outputs during active subnetwork search are printed to a file named “console_out.txt”. If silent_option == FALSE, the output is printed on the screen. Default was set to TRUE because multiple console outputs are simultaneously printed when running in parallel.
Added the list_active_snw_genes parameter to run_pathfindR. When list_active_snw_genes == TRUE, the function adds the column non_DEG_Active_Snw_Genes, which reports the non-DEG active subnetwork genes for the active subnetwork which was enriched for the given pathway with the lowest p value.
Added the data RA_clustered, which is the example output of the clustering workflow.
In the function, run_pathfindR added the option to specify the argument output_dir which specifies the directory to be created under the current working directory for storing the result HTML files. output_dir is “pathfindR_Results” by default.
run_pathfindR now checks whether the output directory (output_dir) already exists and if it exists, stops and displays an error message. This was implemented to prevent writing over existing results.
genes_table.html now contains a second table displaying the input gene symbols for which there were no interactions in the PIN.
+
+
+pathfindR 1.1
+
+Major changes
+- Added the
gene_sets option in run_pathfindR to chose between different gene sets. Available gene sets are KEGG, Reactome, BioCarta and Gene Ontology gene sets (GO-BP, GO-CC and GO-MF)
+-
+
cluster_pathways automatically recognizes the ID type and chooses the gene sets accordingly
+
+
+Minor Changes and Bug Fixes
+- Fixed issue regarding p values < 1e-13. No active subnetworks were found when there were p values < 1e-13. These are now changed to 1e-13 in the function
input_processing
+
+- In
input_processing, genes for which no interactions are found in the PIN are now removed before active subnetwork search
+- Duplicated gene symbols no longer raise an error. If there are duplicated symbols, the lowest p value is chosen for each gene symbol in the function
input_processing
+
+- To prevent the formation of nested folders, by default and on errors, the function
run_pathfindR returns to the user’s working directory.
+- Citation information are now provided for our BioRxiv pre-print
+
+
+
+
+
+
-
+
-
+
-
+
Create UpSet Plot of Enriched Terms
- Source: R/visualization.R
+ Source: R/visualization.R
UpSet_plot.Rd
@@ -98,7 +98,9 @@ Create UpSet Plot of Enriched Terms
Arguments
- - result_df
+
+
+- result_df
A dataframe of pathfindR results that must contain the following columns:
- Term_Description
Description of the enriched term (necessary if use_description = TRUE)
Arguments
- genes_df
+- genes_df
the input data that was used with run_pathfindR.
It must be a data frame with 3 columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (optional)
Arguments
The change values in this data frame are used to color the affected genes
- num_terms
+- num_terms
Number of top enriched terms to use while creating the plot. Set to NULL to use
all enriched terms (default = 10)
- method
+- method
the option for producing the plot. Options include 'heatmap',
'boxplot' and 'barplot'. (default = 'heatmap')
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- low
+- low
a string indicating the color of 'low' values in the coloring gradient (default = 'green')
- mid
+- mid
a string indicating the color of 'mid' values in the coloring gradient (default = 'black')
- high
+- high
a string indicating the color of 'high' values in the coloring gradient (default = 'red')
- ...
+- ...
additional arguments for input_processing (used if
genes_df is provided)
Value
-
-
-UpSet plots are plots of the intersections of sets as a matrix. This
+
UpSet plots are plots of the intersections of sets as a matrix. This
function creates a ggplot object of an UpSet plot where the x-axis is the
UpSet plot of intersections of enriched terms. By default (i.e.
method = 'heatmap') the main plot is a heatmap of genes at the
@@ -192,16 +192,16 @@
Examples
-
+
-
+
-
+
Wrapper for Active Subnetwork Search + Enrichment over Single/Multiple Iteration(s)
- Source: R/utility.R
+ Source: R/utility.R
active_snw_enrichment_wrapper.Rd
@@ -115,122 +115,124 @@ Wrapper for Active Subnetwork Search + Enrichment over Single/Multiple Itera
Arguments
- - input_processed
+
+
+- input_processed
processed input data frame
- pin_path
+- pin_path
path/to/PIN/file
- gset_list
+- gset_list
list for gene sets
- enrichment_threshold
+- enrichment_threshold
adjusted-p value threshold used when filtering
enrichment results (default = 0.05)
- list_active_snw_genes
+- list_active_snw_genes
boolean value indicating whether or not to report
the non-significant active subnetwork genes for the active subnetwork which was enriched for
the given term with the lowest p value (default = FALSE)
- adj_method
+- adj_method
correction method to be used for adjusting p-values.
(default = 'bonferroni')
- search_method
+- search_method
algorithm to use when performing active subnetwork
search. Options are greedy search (GR), simulated annealing (SA) or genetic
algorithm (GA) for the search (default = 'GR').
- disable_parallel
+- disable_parallel
boolean to indicate whether to disable parallel runs
via foreach (default = FALSE)
- use_all_positives
+- use_all_positives
if TRUE: in GA, adds an individual with all positive
nodes. In SA, initializes candidate solution with all positive nodes. (default = FALSE)
- iterations
+- iterations
number of iterations for active subnetwork search and
enrichment analyses (Default = 10)
- n_processes
+- n_processes
optional argument for specifying the number of processes
used by foreach. If not specified, the function determines this
automatically (Default == NULL. Gets set to 1 for Genetic Algorithm)
- score_quan_thr
+- score_quan_thr
active subnetwork score quantile threshold. Must be
between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
- sig_gene_thr
+- sig_gene_thr
threshold for the minimum proportion of significant genes in
the subnetwork (Default = 0.02) If the number of genes to use as threshold is
calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number
is set to 2
- saTemp0
+- saTemp0
Initial temperature for SA (default = 1.0)
- saTemp1
+- saTemp1
Final temperature for SA (default = 0.01)
- saIter
+- saIter
Iteration number for SA (default = 10000)
- gaPop
+- gaPop
Population size for GA (default = 400)
- gaIter
+- gaIter
Iteration number for GA (default = 200)
- gaThread
+- gaThread
Number of threads to be used in GA (default = 5)
- gaCrossover
+- gaCrossover
Applies crossover with the given probability in GA (default = 1, i.e. always perform crossover)
- gaMut
+- gaMut
For GA, applies mutation with given mutation rate (default = 0, i.e. mutation off)
- grMaxDepth
+- grMaxDepth
Sets max depth in greedy search, 0 for no limit (default = 1)
- grSearchDepth
+- grSearchDepth
Search depth in greedy search (default = 1)
- grOverlap
+- grOverlap
Overlap threshold for results of greedy search (default = 0.5)
- grSubNum
+- grSubNum
Number of subnetworks to be presented in the results (default = 1000)
- silent_option
+- silent_option
boolean value indicating whether to print the messages
to the console (FALSE) or not (TRUE, this will print to a temp. file) during
active subnetwork search (default = TRUE). This option was added because
@@ -239,9 +241,7 @@
Arguments
Value
-
-
-Data frame of combined pathfindR enrichment results
+ Data frame of combined pathfindR enrichment results
@@ -256,16 +256,16 @@ Value
-
+
-
+
-
+
Perform Active Subnetwork Search
- Source: R/active_snw_search.R
+ Source: R/active_snw_search.R
active_snw_search.Rd
@@ -112,7 +112,9 @@ Perform Active Subnetwork Search
Arguments
- - input_for_search
+
+
+- input_for_search
input the input data that active subnetwork search uses. The input
must be a data frame containing at least these 2 columns:
- GENE
Gene Symbol
Arguments
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- snws_file
+- snws_file
name for active subnetwork search output data
without file extension (default = 'active_snws')
- dir_for_parallel_run
+- dir_for_parallel_run
(previously created) directory for a parallel run iteration.
Used in the wrapper function (see ?run_pathfindR) (Default = NULL)
- score_quan_thr
+- score_quan_thr
active subnetwork score quantile threshold. Must be
between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
- sig_gene_thr
+- sig_gene_thr
threshold for the minimum proportion of significant genes in
the subnetwork (Default = 0.02) If the number of genes to use as threshold is
calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number
is set to 2
- search_method
+- search_method
algorithm to use when performing active subnetwork
search. Options are greedy search (GR), simulated annealing (SA) or genetic
algorithm (GA) for the search (default = 'GR').
- seedForRandom
+- seedForRandom
seed for reproducibility while running the java modules (applies for GR and SA)
- silent_option
+- silent_option
boolean value indicating whether to print the messages
to the console (FALSE) or not (TRUE, this will print to a temp. file) during
active subnetwork search (default = TRUE). This option was added because
during parallel runs, the console messages get disorderly printed.
- use_all_positives
+- use_all_positives
if TRUE: in GA, adds an individual with all positive
nodes. In SA, initializes candidate solution with all positive nodes. (default = FALSE)
- geneInitProbs
+- geneInitProbs
For SA and GA, probability of adding a gene in initial solution (default = 0.1)
- saTemp0
+- saTemp0
Initial temperature for SA (default = 1.0)
- saTemp1
+- saTemp1
Final temperature for SA (default = 0.01)
- saIter
+- saIter
Iteration number for SA (default = 10000)
- gaPop
+- gaPop
Population size for GA (default = 400)
- gaIter
+- gaIter
Iteration number for GA (default = 200)
- gaThread
+- gaThread
Number of threads to be used in GA (default = 5)
- gaCrossover
+- gaCrossover
Applies crossover with the given probability in GA (default = 1, i.e. always perform crossover)
- gaMut
+- gaMut
For GA, applies mutation with given mutation rate (default = 0, i.e. mutation off)
- grMaxDepth
+- grMaxDepth
Sets max depth in greedy search, 0 for no limit (default = 1)
- grSearchDepth
+- grSearchDepth
Search depth in greedy search (default = 1)
- grOverlap
+- grOverlap
Overlap threshold for results of greedy search (default = 0.5)
- grSubNum
+- grSubNum
Number of subnetworks to be presented in the results (default = 1000)
Value
-
-
-A list of genes in every identified active subnetwork that has a score greater than
+
A list of genes in every identified active subnetwork that has a score greater than
the `score_quan_thr`th quantile and that has at least `sig_gene_thr` affected genes.
@@ -262,16 +262,16 @@ Examples
-
+
-
+
-
+
Annotate the Affected Genes in the Provided Enriched Terms
- Source: R/utility.R
+ Source: R/utility.R
annotate_term_genes.Rd
@@ -92,25 +92,25 @@ Annotate the Affected Genes in the Provided Enriched Terms
Arguments
- - result_df
+
+
+- result_df
data frame of enrichment results.
The only must-have column is 'ID'.
- input_processed
+- input_processed
input data processed via input_processing
- genes_by_term
+- genes_by_term
List that contains genes for each gene set. Names of
this list are gene set IDs (default = kegg_genes)
Value
-
-
-The original data frame with two additional columns:
- Up_regulated
+ The original data frame with two additional columns:
- Up_regulated
the up-regulated genes in the input involved in the given term's gene set, comma-separated
- Down_regulated
@@ -143,16 +143,16 @@ Examples
-
+
-
+
-
+
@@ -88,16 +88,16 @@ Check Java Version
Arguments
- - version
+
+
+- version
character vector containing the output of 'java -version'. If
NULL, result of fetch_java_version is used (default = NULL)
Value
-
-
-only parses and checks whether the java version is >= 1.8
+ only parses and checks whether the java version is >= 1.8
Details
@@ -116,16 +116,16 @@ Details
-
+
-
+
-
+
@@ -95,7 +95,9 @@ Cluster Enriched Terms
Arguments
- - enrichment_res
+
+
+- enrichment_res
data frame of pathfindR enrichment results. Must-have
columns are 'Term_Description' (if use_description = TRUE) or 'ID'
(if use_description = FALSE), 'Down_regulated', and 'Up_regulated'.
@@ -103,29 +105,29 @@
Arguments
provided.- method
+- method
Either 'hierarchical' or 'fuzzy'. Details of clustering are
provided in the corresponding functions hierarchical_term_clustering,
and fuzzy_term_clustering
- plot_clusters_graph
+- plot_clusters_graph
boolean value indicate whether or not to plot
the graph diagram of clustering results (default = TRUE)
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- use_active_snw_genes
+- use_active_snw_genes
boolean to indicate whether or not to use
non-input active subnetwork genes in the calculation of kappa statistics
(default = FALSE, i.e. only use affected genes)
- ...
+- ...
additional arguments for hierarchical_term_clustering,
fuzzy_term_clustering and cluster_graph_vis.
See documentation of these functions for more details.
Arguments
Value
-
-
-a data frame of clustering results. For 'hierarchical', the cluster
+
a data frame of clustering results. For 'hierarchical', the cluster
assignments (Cluster) and whether the term is representative of its cluster
(Status) is added as columns. For 'fuzzy', terms that are in multiple
clusters are provided for each cluster. The cluster assignments (Cluster)
@@ -177,16 +177,16 @@
Examples
-
+
-
+
-
+
Graph Visualization of Clustered Enriched Terms
- Source: R/clustering.R
+ Source: R/clustering.R
cluster_graph_vis.Rd
@@ -96,17 +96,19 @@ Graph Visualization of Clustered Enriched Terms
Arguments
- - clu_obj
+
+
+- clu_obj
clustering result (either a matrix obtained via
hierarchical_term_clustering or fuzzy_term_clustering
`fuzzy_term_clustering` or a vector obtained via `hierarchical_term_clustering`)
- kappa_mat
+- kappa_mat
matrix of kappa statistics (output of create_kappa_matrix)
- enrichment_res
+- enrichment_res
data frame of pathfindR enrichment results. Must-have
columns are 'Term_Description' (if use_description = TRUE) or 'ID'
(if use_description = FALSE), 'Down_regulated', and 'Up_regulated'.
@@ -114,29 +116,27 @@
Arguments
provided.- kappa_threshold
+- kappa_threshold
threshold for kappa statistics, defining strong
relation (default = 0.35)
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- vertex.label.cex
+- vertex.label.cex
font size for vertex labels; it is interpreted as a multiplication factor of some device-dependent base font size (default = 0.7)
- vertex.size.scaling
+- vertex.size.scaling
scaling factor for the node size (default = 2.5)
Value
-
-
-Plots a graph diagram of clustering results. Each node is an enriched term
+
Plots a graph diagram of clustering results. Each node is an enriched term
from `enrichment_res`. Size of node corresponds to -log(lowest_p). Thickness
of the edges between nodes correspond to the kappa statistic between the two
terms. Color of each node corresponds to distinct clusters. For fuzzy
@@ -145,9 +145,9 @@
Value
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
cluster_graph_vis(clu_obj, kappa_mat, enrichment_res)
-}
+} # }
@@ -162,16 +162,16 @@ Examples
-
+
-
+
-
+
Color hsa KEGG pathway
- Source: R/visualization.R
+ Source: R/visualization.R
color_kegg_pathway.Rd
@@ -94,19 +94,21 @@ Color hsa KEGG pathway
Arguments
- - pw_id
+
+
+- pw_id
hsa KEGG pathway id (e.g. hsa05012)
- change_vec
+- change_vec
vector of change values, names should be hsa KEGG gene ids
- scale_vals
+- scale_vals
should change values be scaled? (default = TRUE)
- node_cols
+- node_cols
low, middle and high color values for coloring the pathway nodes
(default = NULL). If node_cols=NULL, the low, middle and high color
are set as 'green', 'gray' and 'red'. If all change values are 1e6 (in case no
@@ -114,26 +116,24 @@
Arguments
input_processing), only one color ('#F38F18' if NULL) is used.- legend.position
+- legend.position
the default position of legends ("none", "left",
"right", "bottom", "top", "inside")
Value
-
-
-a ggplot object containing the colored KEGG pathway diagram visualization
+ a ggplot object containing the colored KEGG pathway diagram visualization
Examples
-
@@ -148,16 +148,16 @@ Examples
-
+
-
+
-
+
Combine 2 pathfindR Results
- Source: R/comparison.R
+ Source: R/comparison.R
combine_pathfindR_results.Rd
@@ -88,24 +88,24 @@ Combine 2 pathfindR Results
Arguments
-
Value
-
-
-Data frame of combined pathfindR enrichment results. Columns are:
- ID
+ Data frame of combined pathfindR enrichment results. Columns are:
- ID
ID of the enriched term
- Term_Description
@@ -160,8 +160,8 @@ Value
Examples
combined_results <- combine_pathfindR_results(example_pathfindR_output, example_comparison_output)
-#> You may run `combined_results_graph()` to create visualizations of combined term-gene graphs of selected terms
+#> You may run `combined_results_graph()` to create visualizations of combined term-gene graphs of selected terms
@@ -176,16 +176,16 @@ Examples
-
+
-
+
-
+
@@ -94,35 +94,35 @@ Combined Results Graph
Arguments
- - combined_df
+
+
+- combined_df
Data frame of combined pathfindR enrichment results
- selected_terms
+- selected_terms
the vector of selected terms for creating the graph
(either IDs or term descriptions). If set to 'common', all of the
common terms are used. (default = 'common')
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- layout
+- layout
The type of layout to create (see ggraph for details. Default = 'stress')
- node_size
+- node_size
Argument to indicate whether to use number of significant genes ('num_genes')
or the -log10(lowest p value) ('p_val') for adjusting the node sizes (default = 'num_genes')
Value
-
-
-a ggraph object containing the combined term-gene graph.
+
a ggraph object containing the combined term-gene graph.
Each node corresponds to an enriched term (orange if common, different shades of blue otherwise),
an up-regulated gene (green), a down-regulated gene (red) or
a conflicting (i.e. up in one analysis, down in the other or vice versa) gene
@@ -155,16 +155,16 @@
Examples
-
+
-
+
-
+
@@ -88,16 +88,16 @@ Configure Output Directory Name
Arguments
-
Value
-
-
-/path/to/output/dir
+ /path/to/output/dir
@@ -112,16 +112,16 @@ Value
-
+
-
+
-
+
Create HTML Report of pathfindR Results
- Source: R/utility.R
+ Source: R/utility.R
create_HTML_report.Rd
@@ -88,7 +88,9 @@ Create HTML Report of pathfindR Results
Arguments
- - input
+
+
+- input
the input data that pathfindR uses. The input must be a data
frame with three columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (OPTIONAL)
Arguments
- input_processed
+- input_processed
processed input data frame
- final_res
+- final_res
final pathfindR result data frame
- dir_for_report
+- dir_for_report
directory to render the report in
@@ -121,16 +123,16 @@ Arguments
-
+
-
+
-
+
Create Kappa Statistics Matrix
- Source: R/clustering.R
+ Source: R/clustering.R
create_kappa_matrix.Rd
@@ -92,7 +92,9 @@ Create Kappa Statistics Matrix
Arguments
- - enrichment_res
+
+
+- enrichment_res
data frame of pathfindR enrichment results. Must-have
columns are 'Term_Description' (if use_description = TRUE) or 'ID'
(if use_description = FALSE), 'Down_regulated', and 'Up_regulated'.
@@ -100,12 +102,12 @@
Arguments
provided.- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- use_active_snw_genes
+- use_active_snw_genes
boolean to indicate whether or not to use
non-input active subnetwork genes in the calculation of kappa statistics
(default = FALSE, i.e. only use affected genes)
Arguments
Value
-
-
-a matrix of kappa statistics between each term in the
+
a matrix of kappa statistics between each term in the
enrichment results.
@@ -141,16 +141,16 @@ Examples
-
+
-
+
-
+
Perform Enrichment Analysis for a Single Gene Set
- Source: R/enrichment.R
+ Source: R/enrichment.R
enrichment.Rd
@@ -96,38 +96,40 @@ Perform Enrichment Analysis for a Single Gene Set
Arguments
- - input_genes
+
+
+- input_genes
The set of gene symbols to be used for enrichment
analysis. In the scope of this package, these are genes that were
identified for an active subnetwork
- genes_by_term
+- genes_by_term
List that contains genes for each gene set. Names of
this list are gene set IDs (default = kegg_genes)
- term_descriptions
+- term_descriptions
Vector that contains term descriptions for the
gene sets. Names of this vector are gene set IDs (default = kegg_descriptions)
- adj_method
+- adj_method
correction method to be used for adjusting p-values.
(default = 'bonferroni')
- enrichment_threshold
+- enrichment_threshold
adjusted-p value threshold used when filtering
enrichment results (default = 0.05)
- sig_genes_vec
+- sig_genes_vec
vector of significant gene symbols. In the scope of this
package, these are the input genes that were used for active subnetwork search
- background_genes
+- background_genes
vector of background genes. In the scope of this package,
the background genes are taken as all genes in the PIN
(see enrichment_analyses)
Arguments
Value
-
-
-A data frame that contains enrichment results
+ A data frame that contains enrichment results
See also
@@ -174,16 +174,16 @@ Examples
-
+
-
+
-
+
Perform Enrichment Analyses on the Input Subnetworks
- Source: R/enrichment.R
+ Source: R/enrichment.R
enrichment_analyses.Rd
@@ -97,42 +97,44 @@ Perform Enrichment Analyses on the Input Subnetworks
Arguments
- - snws
+
+
+- snws
a list of subnetwork genes (i.e., vectors of genes for each subnetwork)
- sig_genes_vec
+- sig_genes_vec
vector of significant gene symbols. In the scope of this
package, these are the input genes that were used for active subnetwork search
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- genes_by_term
+- genes_by_term
List that contains genes for each gene set. Names of
this list are gene set IDs (default = kegg_genes)
- term_descriptions
+- term_descriptions
Vector that contains term descriptions for the
gene sets. Names of this vector are gene set IDs (default = kegg_descriptions)
- adj_method
+- adj_method
correction method to be used for adjusting p-values.
(default = 'bonferroni')
- enrichment_threshold
+- enrichment_threshold
adjusted-p value threshold used when filtering
enrichment results (default = 0.05)
- list_active_snw_genes
+- list_active_snw_genes
boolean value indicating whether or not to report
the non-significant active subnetwork genes for the active subnetwork which was enriched for
the given term with the lowest p value (default = FALSE)
Arguments
Value
-
-
-a dataframe of combined enrichment results. Columns are:
- ID
+ a dataframe of combined enrichment results. Columns are:
- ID
ID of the enriched term
- Term_Description
@@ -191,16 +191,16 @@ Examples
-
+
-
+
-
+
Create Bubble Chart of Enrichment Results
- Source: R/visualization.R
+ Source: R/visualization.R
enrichment_chart.Rd
@@ -96,7 +96,9 @@ Create Bubble Chart of Enrichment Results
Arguments
- - result_df
+
+
+- result_df
a data frame that must contain the following columns:
- Term_Description
Description of the enriched term
Arguments
- top_terms
+- top_terms
number of top terms (according to the 'lowest_p' column)
to plot (default = 10). If plot_by_cluster = TRUE, selects the top
top_terms terms per each cluster. Set top_terms = NULL to plot
@@ -127,18 +129,18 @@
Arguments
all terms are plotted.- plot_by_cluster
+- plot_by_cluster
boolean value indicating whether or not to group the
enriched terms by cluster (works if result_df contains a
'Cluster' column).
- num_bubbles
+- num_bubbles
number of sizes displayed in the legend # genes
(Default = 4)
- even_breaks
+- even_breaks
whether or not to set even breaks for the number of sizes
displayed in the legend # genes. If TRUE (default), sets
equal breaks and the number of displayed bubbles may be different than the
@@ -148,9 +150,7 @@
Arguments
Value
-
-
-a ggplot2 object containing the bubble chart.
+
a ggplot2 object containing the bubble chart.
The x-axis corresponds to fold enrichment values while the y-axis indicates
the enriched terms. Size of the bubble indicates the number of significant
genes in the given enriched term. Color indicates the -log10(lowest-p) value.
@@ -177,16 +177,16 @@
Examples
-
+
-
+
-
+
@@ -96,7 +96,9 @@ Fetch Gene Set Objects
Arguments
- - gene_sets
+
+
+- gene_sets
Name of the gene sets to be used for enrichment analysis.
Available gene sets are 'KEGG', 'Reactome', 'BioCarta', 'GO-All',
'GO-BP', 'GO-CC', 'GO-MF', 'cell_markers', 'mmu_KEGG' or 'Custom'.
@@ -104,21 +106,21 @@
Arguments
must be specified. (Default = 'KEGG')- min_gset_size
+- min_gset_size
minimum number of genes a term must contain (default = 10)
- max_gset_size
+- max_gset_size
maximum number of genes a term must contain (default = 300)
- custom_genes
+- custom_genes
a list containing the genes involved in each custom
term. Each element is a vector of gene symbols located in the given custom
term. Names should correspond to the IDs of the custom terms.
- custom_descriptions
+- custom_descriptions
A vector containing the descriptions for each
custom term. Names of the vector should correspond to the IDs of the custom
terms.
Arguments
Value
-
-
-a list containing 2 elements
- genes_by_term
+ a list containing 2 elements
- genes_by_term
list of vectors of genes contained in each term
- term_descriptions
@@ -155,16 +155,16 @@ Examples
-
+
-
+
-
+
@@ -88,9 +88,7 @@ Obtain Java Version
Value
-
-
-character vector containing the output of 'java -version'
+ character vector containing the output of 'java -version'
Details
@@ -109,16 +107,16 @@ Details
-
+
-
+
-
+
Parse Active Subnetwork Search Output File and Filter the Subnetworks
- Source: R/active_snw_search.R
+ Source: R/active_snw_search.R
filterActiveSnws.Rd
@@ -93,21 +93,23 @@ Parse Active Subnetwork Search Output File and Filter the Subnetworks
Arguments
- - active_snw_path
+
+
+- active_snw_path
path to the output of an Active Subnetwork Search
- sig_genes_vec
+- sig_genes_vec
vector of significant gene symbols. In the scope of this
package, these are the input genes that were used for active subnetwork search
- score_quan_thr
+- score_quan_thr
active subnetwork score quantile threshold. Must be
between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
- sig_gene_thr
+- sig_gene_thr
threshold for the minimum proportion of significant genes in
the subnetwork (Default = 0.02) If the number of genes to use as threshold is
calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number
@@ -116,9 +118,7 @@
Arguments
Value
-
-
-A list containing subnetworks: a list of of genes in every
+
A list containing subnetworks: a list of of genes in every
active subnetwork that has a score greater than the score_quan_thrth
quantile and that contains at least sig_gene_thr of significant genes
and scores the score of each filtered active subnetwork
@@ -153,16 +153,16 @@ Examples
-
+
-
+
-
+
Heuristic Fuzzy Multiple-linkage Partitioning of Enriched Terms
- Source: R/clustering.R
+ Source: R/clustering.R
fuzzy_term_clustering.Rd
@@ -93,11 +93,13 @@ Heuristic Fuzzy Multiple-linkage Partitioning of Enriched Terms
Arguments
- - kappa_mat
+
+
+- kappa_mat
matrix of kappa statistics (output of create_kappa_matrix)
- enrichment_res
+- enrichment_res
data frame of pathfindR enrichment results. Must-have
columns are 'Term_Description' (if use_description = TRUE) or 'ID'
(if use_description = FALSE), 'Down_regulated', and 'Up_regulated'.
@@ -105,21 +107,19 @@
Arguments
provided.- kappa_threshold
+- kappa_threshold
threshold for kappa statistics, defining strong
relation (default = 0.35)
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
Value
-
-
-a boolean matrix of cluster assignments. Each row corresponds to an
+
a boolean matrix of cluster assignments. Each row corresponds to an
enriched term, each column corresponds to a cluster.
@@ -132,10 +132,10 @@ Details
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
fuzzy_term_clustering(kappa_mat, enrichment_res)
fuzzy_term_clustering(kappa_mat, enrichment_res, kappa_threshold = 0.45)
-}
+} # }
@@ -150,16 +150,16 @@ Examples
-
+
-
+
-
+
Retrieve the Requested Release of Organism-specific BioGRID PIN
- Source: R/data_generation.R
+ Source: R/data_generation.R
get_biogrid_pin.Rd
@@ -88,27 +88,27 @@ Retrieve the Requested Release of Organism-specific BioGRID PIN
Arguments
- - org
+
+
+- org
organism name. BioGRID naming requires underscores for spaces so
'Homo sapiens' becomes 'Homo_sapiens', 'Mus musculus' becomes 'Mus_musculus'
etc. See https://wiki.thebiogrid.org/doku.php/statistics for a full
list of available organisms (default = 'Homo_sapiens')
- path2pin
+- path2pin
the path of the file to save the PIN data. By default, the
PIN data is saved in a temporary file
- release
+- release
the requested BioGRID release (default = 'latest')
Value
-
-
-the path of the file in which the PIN data was saved. If
+
the path of the file in which the PIN data was saved. If
path2pin was not supplied by the user, the PIN data is saved in a
temporary file
@@ -125,16 +125,16 @@ Value
-
+
-
+
-
+
Retrieve Organism-specific Gene Sets List
- Source: R/data_generation.R
+ Source: R/data_generation.R
get_gene_sets_list.Rd
@@ -94,35 +94,35 @@ Retrieve Organism-specific Gene Sets List
Arguments
- - source
+
+
+- source
As of this version, either 'KEGG', 'Reactome' or 'MSigDB' (default = 'KEGG')
- org_code
+- org_code
(Used for 'KEGG' only) KEGG organism code for the selected organism. For a full list
of all available organisms, see https://www.genome.jp/kegg/catalog/org_list.html
- species
+- species
(Used for 'MSigDB' only) species name, such as Homo sapiens, Mus musculus, etc.
See msigdbr_show_species for all the species available in
the msigdbr package (default = 'Homo sapiens')
- collection
+- collection
(Used for 'MSigDB' only) collection. i.e., H, C1, C2, C3, C4, C5, C6, C7.
- subcollection
+- subcollection
(Used for 'MSigDB' only) sub-collection, such as CGP, MIR, BP, etc. (default = NULL,
i.e. list all gene sets in collection)
Value
-
-
-A list containing 2 elements:
gene_sets - A list containing the genes involved in each gene set
A list containing 2 elements:
gene_sets - A list containing the genes involved in each gene set
descriptions - A named vector containing the descriptions for each gene set
. For 'KEGG' and 'MSigDB', it is possible to choose a specific organism. For a full list
of all available KEGG organisms, see https://www.genome.jp/kegg/catalog/org_list.html.
@@ -143,16 +143,16 @@
Value
-
+
-
+
-
+
Retrieve Organism-specific KEGG Pathway Gene Sets
- Source: R/data_generation.R
+ Source: R/data_generation.R
get_kegg_gsets.Rd
@@ -88,16 +88,16 @@ Retrieve Organism-specific KEGG Pathway Gene Sets
Arguments
- - org_code
+
+
+- org_code
KEGG organism code for the selected organism. For a full list
of all available organisms, see https://www.genome.jp/kegg/catalog/org_list.html
Value
-
-
-list containing 2 elements:
gene_sets - A list containing KEGG IDs for the genes involved in each KEGG pathway
list containing 2 elements:
gene_sets - A list containing KEGG IDs for the genes involved in each KEGG pathway
descriptions - A named vector containing the descriptions for each KEGG pathway
@@ -113,16 +113,16 @@ Value
-
+
-
+
-
+
Retrieve Organism-specific MSigDB Gene Sets
- Source: R/data_generation.R
+ Source: R/data_generation.R
get_mgsigdb_gsets.Rd
@@ -88,26 +88,26 @@ Retrieve Organism-specific MSigDB Gene Sets
Arguments
- - species
+
+
+- species
species name, such as Homo sapiens, Mus musculus, etc.
See msigdbr_show_species for all the species available in
the msigdbr package
- collection
+- collection
collection. i.e., H, C1, C2, C3, C4, C5, C6, C7.
- subcollection
+- subcollection
sub-collection, such as CGP, BP, etc. (default = NULL,
i.e. list all gene sets in collection)
Value
-
-
-Retrieves the MSigDB gene sets and returns a list containing 2 elements:
gene_sets - A list containing the genes involved in each of the selected MSigDB gene sets
Retrieves the MSigDB gene sets and returns a list containing 2 elements:
gene_sets - A list containing the genes involved in each of the selected MSigDB gene sets
descriptions - A named vector containing the descriptions for each selected MSigDB gene set
@@ -133,16 +133,16 @@ Details
-
+
-
+
-
+
Retrieve Organism-specific PIN data
- Source: R/data_generation.R
+ Source: R/data_generation.R
get_pin_file.Rd
@@ -88,42 +88,42 @@ Retrieve Organism-specific PIN data
Arguments
- - source
+
+
+- source
As of this version, this function is implemented to get data
from 'BioGRID' only. This argument (and this wrapper function) was implemented
for future utility
- org
+- org
organism name. BioGRID naming requires underscores for spaces so
'Homo sapiens' becomes 'Homo_sapiens', 'Mus musculus' becomes 'Mus_musculus'
etc. See https://wiki.thebiogrid.org/doku.php/statistics for a full
list of available organisms (default = 'Homo_sapiens')
- path2pin
+- path2pin
the path of the file to save the PIN data. By default, the
PIN data is saved in a temporary file
- ...
+- ...
additional arguments for get_biogrid_pin
Value
-
-
-the path of the file in which the PIN data was saved. If
+
the path of the file in which the PIN data was saved. If
path2pin was not supplied by the user, the PIN data is saved in a
temporary file
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
pin_path <- get_pin_file()
-}
+} # }
@@ -138,16 +138,16 @@ Examples
-
+
-
+
-
+
Retrieve Reactome Pathway Gene Sets
- Source: R/data_generation.R
+ Source: R/data_generation.R
get_reactome_gsets.Rd
@@ -88,9 +88,7 @@ Retrieve Reactome Pathway Gene Sets
Value
-
-
-Gets the latest Reactome pathways gene sets in gmt format. Parses the
+
Gets the latest Reactome pathways gene sets in gmt format. Parses the
gmt file and returns a list containing 2 elements:
gene_sets - A list containing the genes involved in each Reactome pathway
descriptions - A named vector containing the descriptions for each Reactome pathway
@@ -107,16 +105,16 @@ Value
-
+
-
+
-
+
Retrieve Gene Sets from GMT-format File
- Source: R/data_generation.R
+ Source: R/data_generation.R
gset_list_from_gmt.Rd
@@ -88,19 +88,19 @@ Retrieve Gene Sets from GMT-format File
Arguments
-
Value
-
-
-list containing 2 elements:
gene_sets - A list containing the genes involved in each gene set
list containing 2 elements:
gene_sets - A list containing the genes involved in each gene set
descriptions - A named vector containing the descriptions for each gene set
@@ -116,16 +116,16 @@ Value
-
+
-
+
-
+
Hierarchical Clustering of Enriched Terms
- Source: R/clustering.R
+ Source: R/clustering.R
hierarchical_term_clustering.Rd
@@ -96,11 +96,13 @@ Hierarchical Clustering of Enriched Terms
Arguments
- - kappa_mat
+
+
+- kappa_mat
matrix of kappa statistics (output of create_kappa_matrix)
- enrichment_res
+- enrichment_res
data frame of pathfindR enrichment results. Must-have
columns are 'Term_Description' (if use_description = TRUE) or 'ID'
(if use_description = FALSE), 'Down_regulated', and 'Up_regulated'.
@@ -108,37 +110,35 @@
Arguments
provided.- num_clusters
+- num_clusters
number of clusters to be formed (default = NULL).
If NULL, the optimal number of clusters is determined as the number
which yields the highest average silhouette width.
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- clu_method
+- clu_method
the agglomeration method to be used
(default = 'average', see hclust)
- plot_hmap
+- plot_hmap
boolean to indicate whether to plot the kappa statistics
clustering heatmap or not (default = FALSE)
- plot_dend
+- plot_dend
boolean to indicate whether to plot the clustering
dendrogram partitioned into the optimal number of clusters (default = TRUE)
Value
-
-
-a vector of clusters for each enriched term in the enrichment results.
+ a vector of clusters for each enriched term in the enrichment results.
Details
@@ -153,10 +153,10 @@ Details
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
hierarchical_term_clustering(kappa_mat, enrichment_res)
hierarchical_term_clustering(kappa_mat, enrichment_res, method = 'complete')
-}
+} # }
@@ -171,16 +171,16 @@ Examples
-
+
-
+
-
+
Hypergeometric Distribution-based Hypothesis Testing
- Source: R/enrichment.R
+ Source: R/enrichment.R
hyperg_test.Rd
@@ -88,24 +88,24 @@ Hypergeometric Distribution-based Hypothesis Testing
Arguments
-
Value
-
-
-the p-value as determined using the hypergeometric distribution.
+ the p-value as determined using the hypergeometric distribution.
Details
@@ -137,16 +137,16 @@ Examples
-
+
Package index • pathfindR
-
+
-
+
-
+
@@ -93,7 +93,9 @@ Process Input
Arguments
- - input
+
+
+- input
the input data that pathfindR uses. The input must be a data
frame with three columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (OPTIONAL)
Arguments
- p_val_threshold
+- p_val_threshold
the p value threshold to use when filtering
the input data frame. Must a numeric value between 0 and 1. (default = 0.05)
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- convert2alias
+- convert2alias
boolean to indicate whether or not to convert gene symbols
in the input that are not found in the PIN to an alias symbol found in the PIN
(default = TRUE) IMPORTANT NOTE: the conversion uses human gene symbols/alias symbols.
Arguments
Value
-
-
-This function first filters the input so that all p values are less
+
This function first filters the input so that all p values are less
than or equal to the threshold. Next, gene symbols that are not found in
the PIN are identified. If aliases of these gene symbols are found in the
PIN, the symbols are converted to the corresponding aliases. The
@@ -173,16 +173,16 @@
Examples
-
+
-
+
-
+
@@ -88,7 +88,9 @@ Input Testing
Arguments
- - input
+
+
+- input
the input data that pathfindR uses. The input must be a data
frame with three columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (OPTIONAL)
Arguments
- p_val_threshold
+- p_val_threshold
the p value threshold to use when filtering
the input data frame. Must a numeric value between 0 and 1. (default = 0.05)
Value
-
-
-Only checks if the input and the threshold follows the required
+
Only checks if the input and the threshold follows the required
specifications.
@@ -133,16 +133,16 @@ Examples
-
+
-
+
-
+
@@ -88,15 +88,15 @@ Check if value is a valid color
Value
-
-
-TRUE if x is a valid color, otherwise FALSE
+ TRUE if x is a valid color, otherwise FALSE
@@ -111,16 +111,16 @@ Value
-
+
-
+
-
+
pathfindR: A package for Enrichment Analysis Utilizing Active Subnetworks
- Source: R/pathfindr.R
+ Source: R/pathfindr.R
pathfindr.Rd
@@ -138,16 +138,16 @@ Author
-
+
-
+
-
+
Plot the Heatmap of Score Matrix of Enriched Terms per Sample
- Source: R/scoring.R
+ Source: R/scoring.R
plot_scores.Rd
@@ -97,46 +97,46 @@ Plot the Heatmap of Score Matrix of Enriched Terms per Sample
Arguments
- - score_matrix
+
+
+- score_matrix
Matrix of agglomerated enriched term scores per sample. Columns are
samples, rows are enriched terms
- cases
+- cases
(Optional) A vector of sample names that are cases in the
case/control experiment. (default = NULL)
- label_samples
+- label_samples
Boolean value to indicate whether or not to label the
samples in the heatmap plot (default = TRUE)
- case_title
+- case_title
Naming of the 'Case' group (as in cases) (default = 'Case')
- control_title
+- control_title
Naming of the 'Control' group (default = 'Control')
- low
+- low
a string indicating the color of 'low' values in the coloring gradient (default = 'green')
- mid
+- mid
a string indicating the color of 'mid' values in the coloring gradient (default = 'black')
- high
+- high
a string indicating the color of 'high' values in the coloring gradient (default = 'red')
Value
-
-
-A `ggplot2` object containing the heatmap plot. x-axis indicates
+
A `ggplot2` object containing the heatmap plot. x-axis indicates
the samples. y-axis indicates the enriched terms. 'Score' indicates the
score of the term in a given sample. If cases are provided, the plot is
divided into 2 facets, named by case_title and control_title.
@@ -164,16 +164,16 @@ Examples
-
+
-
+
-
+
Process Data frame of Protein-protein Interactions
- Source: R/data_generation.R
+ Source: R/data_generation.R
process_pin.Rd
@@ -88,16 +88,16 @@ Process Data frame of Protein-protein Interactions
Arguments
-
Value
-
-
-processed PIN data frame (removes self-interactions and
+
processed PIN data frame (removes self-interactions and
duplicated interactions)
@@ -113,16 +113,16 @@ Value
-
+
-
+
-
+
Return The Path to Given Protein-Protein Interaction Network (PIN)
- Source: R/utility.R
+ Source: R/utility.R
return_pin_path.Rd
@@ -100,7 +100,9 @@ Return The Path to Given Protein-Protein Interaction Network (PIN)
Arguments
- - pin_name_path
+
+
+
Value
-
-
-The absolute path to chosen PIN.
+ The absolute path to chosen PIN.
See also
@@ -120,9 +120,9 @@ See also
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
pin_path <- return_pin_path('GeneMania')
-}
+} # }
@@ -137,16 +137,16 @@ Examples
-
+
-
+
-
+
Wrapper Function for pathfindR - Active-Subnetwork-Oriented Enrichment Workflow
- Source: R/core.R
+ Source: R/core.R
run_pathfindr.Rd
@@ -103,7 +103,9 @@ Wrapper Function for pathfindR - Active-Subnetwork-Oriented Enrichment Workf
Arguments
- - input
+
+
+- input
the input data that pathfindR uses. The input must be a data
frame with three columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (OPTIONAL)
Arguments
- gene_sets
+- gene_sets
Name of the gene sets to be used for enrichment analysis.
Available gene sets are 'KEGG', 'Reactome', 'BioCarta', 'GO-All',
'GO-BP', 'GO-CC', 'GO-MF', 'cell_markers', 'mmu_KEGG' or 'Custom'.
@@ -119,73 +121,71 @@
Arguments
must be specified. (Default = 'KEGG')- min_gset_size
+- min_gset_size
minimum number of genes a term must contain (default = 10)
- max_gset_size
+- max_gset_size
maximum number of genes a term must contain (default = 300)
- custom_genes
+- custom_genes
a list containing the genes involved in each custom
term. Each element is a vector of gene symbols located in the given custom
term. Names should correspond to the IDs of the custom terms.
- custom_descriptions
+- custom_descriptions
A vector containing the descriptions for each
custom term. Names of the vector should correspond to the IDs of the custom
terms.
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- p_val_threshold
+- p_val_threshold
the p value threshold to use when filtering
the input data frame. Must a numeric value between 0 and 1. (default = 0.05)
- enrichment_threshold
+- enrichment_threshold
adjusted-p value threshold used when filtering
enrichment results (default = 0.05)
- convert2alias
+- convert2alias
boolean to indicate whether or not to convert gene symbols
in the input that are not found in the PIN to an alias symbol found in the PIN
(default = TRUE) IMPORTANT NOTE: the conversion uses human gene symbols/alias symbols.
- plot_enrichment_chart
+- plot_enrichment_chart
boolean value. If TRUE, a bubble chart displaying
the enrichment results is plotted. (default = TRUE)
- output_dir
+- output_dir
the directory to be created where the output and intermediate
files are saved (default = NULL, a temporary directory is used)
- list_active_snw_genes
+- list_active_snw_genes
boolean value indicating whether or not to report
the non-significant active subnetwork genes for the active subnetwork which was enriched for
the given term with the lowest p value (default = FALSE)
- ...
+- ...
additional arguments for active_snw_enrichment_wrapper
Value
-
-
-Data frame of pathfindR enrichment results. Columns are:
- ID
+ Data frame of pathfindR enrichment results. Columns are:
- ID
ID of the enriched term
- Term_Description
@@ -220,8 +220,6 @@ Value
results linked to the visualizations of the enriched terms in addition to
the table of converted gene symbols. This report can be found in
'output_dir/results.html' under the current working directory.
-
-
By default, a bubble chart of top 10 enrichment results are plotted. The x-axis
corresponds to fold enrichment values while the y-axis indicates the enriched
terms. Sizes of the bubbles indicate the number of significant genes in the given terms.
@@ -263,9 +261,9 @@
See also
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
run_pathfindR(example_pathfindR_input)
-}
+} # }
@@ -280,16 +278,16 @@ Examples
-
+
-
+
-
+
Calculate Agglomerated Scores of Enriched Terms for Each Subject
- Source: R/scoring.R
+ Source: R/scoring.R
score_terms.Rd
@@ -95,7 +95,9 @@ Calculate Agglomerated Scores of Enriched Terms for Each Subject
Arguments
- - enrichment_table
+
+
+- enrichment_table
a data frame that must contain the 3 columns below:
- Term_Description
Description of the enriched term (necessary if use_description = TRUE)
Arguments
- exp_mat
+- exp_mat
the experiment (e.g., gene expression/methylation) matrix.
Columns are samples and rows are genes. Column names must contain sample
names and row names must contain the gene symbols.
- cases
+- cases
(Optional) A vector of sample names that are cases in the
case/control experiment. (default = NULL)
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- plot_hmap
+- plot_hmap
Boolean value to indicate whether or not to draw the
heatmap plot of the scores. (default = TRUE)
- ...
+- ...
Additional arguments for plot_scores for aesthetics
of the heatmap plot
Value
-
-
-Matrix of agglomerated scores of each enriched term per sample.
+
Matrix of agglomerated scores of each enriched term per sample.
Columns are samples, rows are enriched terms. Optionally, displays a heatmap
of this matrix.
Conceptual Background
-
+
For an experiment matrix (containing expression, methylation, etc. values),
the rows of which are genes and the columns of which are samples,
@@ -193,16 +193,16 @@
Examples
-
+
-
+
-
+
Active Subnetwork Search + Enrichment Analysis Wrapper for a Single Iteration
- Source: R/utility.R
+ Source: R/utility.R
single_iter_wrapper.Rd
@@ -115,119 +115,121 @@ Active Subnetwork Search + Enrichment Analysis Wrapper for a Single Iteratio
Arguments
- - i
+
+
+- i
current iteration index (default = NULL)
- dirs
+- dirs
vector of directories for parallel runs
- input_processed
+- input_processed
processed input data frame
- pin_path
+- pin_path
path/to/PIN/file
- score_quan_thr
+- score_quan_thr
active subnetwork score quantile threshold. Must be
between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
- sig_gene_thr
+- sig_gene_thr
threshold for the minimum proportion of significant genes in
the subnetwork (Default = 0.02) If the number of genes to use as threshold is
calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number
is set to 2
- search_method
+- search_method
algorithm to use when performing active subnetwork
search. Options are greedy search (GR), simulated annealing (SA) or genetic
algorithm (GA) for the search (default = 'GR').
- silent_option
+- silent_option
boolean value indicating whether to print the messages
to the console (FALSE) or not (TRUE, this will print to a temp. file) during
active subnetwork search (default = TRUE). This option was added because
during parallel runs, the console messages get disorderly printed.
- use_all_positives
+- use_all_positives
if TRUE: in GA, adds an individual with all positive
nodes. In SA, initializes candidate solution with all positive nodes. (default = FALSE)
- geneInitProbs
+- geneInitProbs
For SA and GA, probability of adding a gene in initial solution (default = 0.1)
- saTemp0
+- saTemp0
Initial temperature for SA (default = 1.0)
- saTemp1
+- saTemp1
Final temperature for SA (default = 0.01)
- saIter
+- saIter
Iteration number for SA (default = 10000)
- gaPop
+- gaPop
Population size for GA (default = 400)
- gaIter
+- gaIter
Iteration number for GA (default = 200)
- gaThread
+- gaThread
Number of threads to be used in GA (default = 5)
- gaCrossover
+- gaCrossover
Applies crossover with the given probability in GA (default = 1, i.e. always perform crossover)
- gaMut
+- gaMut
For GA, applies mutation with given mutation rate (default = 0, i.e. mutation off)
- grMaxDepth
+- grMaxDepth
Sets max depth in greedy search, 0 for no limit (default = 1)
- grSearchDepth
+- grSearchDepth
Search depth in greedy search (default = 1)
- grOverlap
+- grOverlap
Overlap threshold for results of greedy search (default = 0.5)
- grSubNum
+- grSubNum
Number of subnetworks to be presented in the results (default = 1000)
- gset_list
+- gset_list
list for gene sets
- adj_method
+- adj_method
correction method to be used for adjusting p-values.
(default = 'bonferroni')
- enrichment_threshold
+- enrichment_threshold
adjusted-p value threshold used when filtering
enrichment results (default = 0.05)
- list_active_snw_genes
+- list_active_snw_genes
boolean value indicating whether or not to report
the non-significant active subnetwork genes for the active subnetwork which was enriched for
the given term with the lowest p value (default = FALSE)
Arguments
Value
-
-
-Data frame of enrichment results using active subnetwork search results
+ Data frame of enrichment results using active subnetwork search results
@@ -252,16 +252,16 @@ Value
-
+
-
+
-
+
Summarize Enrichment Results
- Source: R/enrichment.R
+ Source: R/enrichment.R
summarize_enrichment_results.Rd
@@ -88,7 +88,9 @@ Summarize Enrichment Results
Arguments
- - enrichment_res
+
+
+- enrichment_res
a dataframe of combined enrichment results. Columns are:
- ID
ID of the enriched term
Arguments
- list_active_snw_genes
+- list_active_snw_genes
boolean value indicating whether or not to report
the non-significant active subnetwork genes for the active subnetwork which was enriched for
the given term with the lowest p value (default = FALSE)
Arguments
Value
-
-
-a dataframe of summarized enrichment results (over multiple iterations). Columns are:
- ID
+ a dataframe of summarized enrichment results (over multiple iterations). Columns are:
- ID
ID of the enriched term
- Term_Description
@@ -150,9 +150,9 @@ Value
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
summarize_enrichment_results(enrichment_res)
-}
+} # }
@@ -167,16 +167,16 @@ Examples
-
+
-
+
-
+
@@ -95,7 +95,9 @@ Create Term-Gene Graph
Arguments
- - result_df
+
+
+- result_df
A dataframe of pathfindR results that must contain the following columns:
- Term_Description
Description of the enriched term (necessary if use_description = TRUE)
Arguments
- num_terms
+- num_terms
Number of top enriched terms to use while creating the graph. Set to NULL to use
all enriched terms (default = 10, i.e. top 10 terms)
- layout
+- layout
The type of layout to create (see ggraph for details. Default = 'stress')
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- node_size
+- node_size
Argument to indicate whether to use number of significant genes ('num_genes')
or the -log10(lowest p value) ('p_val') for adjusting the node sizes (default = 'num_genes')
- node_colors
+- node_colors
vector of 3 colors to be used for coloring nodes (colors for term nodes, up, and down, respectively)
Value
-
-
-a ggraph object containing the term-gene graph.
+
a ggraph object containing the term-gene graph.
Each node corresponds to an enriched term (beige), an up-regulated gene (green)
or a down-regulated gene (red). An edge between a term and a gene indicates
that the given term involves the gene. Size of a term node is proportional
@@ -179,16 +179,16 @@
Examples
-
+
-
+
-
+
Create Terms by Genes Heatmap
- Source: R/visualization.R
+ Source: R/visualization.R
term_gene_heatmap.Rd
@@ -99,7 +99,9 @@ Create Terms by Genes Heatmap
Arguments
- - result_df
+
+
+- result_df
A dataframe of pathfindR results that must contain the following columns:
- Term_Description
Description of the enriched term (necessary if use_description = TRUE)
Arguments
- genes_df
+- genes_df
the input data that was used with run_pathfindR.
It must be a data frame with 3 columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (optional)
Arguments
The change values in this data frame are used to color the affected genes
- num_terms
+- num_terms
Number of top enriched terms to use while creating the plot. Set to NULL to use
all enriched terms (default = 10)
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- low
+- low
a string indicating the color of 'low' values in the coloring gradient (default = 'green')
- mid
+- mid
a string indicating the color of 'mid' values in the coloring gradient (default = 'black')
- high
+- high
a string indicating the color of 'high' values in the coloring gradient (default = 'red')
- legend_title
+- legend_title
legend title (default = 'change')
- sort_terms_by_p
+- sort_terms_by_p
boolean to indicate whether to sort terms by 'lowest_p'
(TRUE) or by number of genes (FALSE) (default = FALSE)
- ...
+- ...
additional arguments for input_processing (used if
genes_df is provided)
Value
-
-
-a ggplot2 object of a heatmap where rows are enriched terms and
+
a ggplot2 object of a heatmap where rows are enriched terms and
columns are involved input genes. If genes_df is provided, colors of
the tiles indicate the change values.
@@ -190,16 +190,16 @@ Examples
-
+
-
+
-
+
Visualize Human KEGG Pathways
- Source: R/visualization.R
+ Source: R/visualization.R
visualize_KEGG_diagram.Rd
@@ -94,19 +94,21 @@ Visualize Human KEGG Pathways
Arguments
- - kegg_pw_ids
+
+
+- kegg_pw_ids
KEGG ids of pathways to be colored and visualized
- input_processed
+- input_processed
input data processed via input_processing
- scale_vals
+- scale_vals
should change values be scaled? (default = TRUE)
- node_cols
+- node_cols
low, middle and high color values for coloring the pathway nodes
(default = NULL). If node_cols=NULL, the low, middle and high color
are set as 'green', 'gray' and 'red'. If all change values are 1e6 (in case no
@@ -114,16 +116,14 @@
Arguments
input_processing), only one color ('#F38F18' if NULL) is used.- legend.position
+- legend.position
the default position of legends ("none", "left",
"right", "bottom", "top", "inside")
Value
-
-
-Creates colored visualizations of the enriched human KEGG pathways
+
Creates colored visualizations of the enriched human KEGG pathways
and returns them as a list of ggplot objects, named by Term ID.
@@ -135,13 +135,13 @@ See also
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
input_processed <- data.frame(
GENE = c("PKLR", "GPI", "CREB1", "INS"),
CHANGE = c(1.5, -2, 3, 5)
)
gg_list <- visualize_KEGG_diagram(c("hsa00010", "hsa04911"), input_processed)
-}
+} # }
@@ -156,16 +156,16 @@ Examples
-
+
-
+
-
+
Visualize Active Subnetworks
- Source: R/active_snw_search.R
+ Source: R/active_snw_search.R
visualize_active_subnetworks.Rd
@@ -97,11 +97,13 @@ Visualize Active Subnetworks
Arguments
- - active_snw_path
+
+
+- active_snw_path
path to the output of an Active Subnetwork Search
- genes_df
+- genes_df
the input data that was used with run_pathfindR.
It must be a data frame with 3 columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (optional)
Arguments
The change values in this data frame are used to color the affected genes
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- num_snws
+- num_snws
number of top subnetworks to be visualized (leave blank if
you want to visualize all subnetworks)
- layout
+- layout
The type of layout to create (see ggraph for details. Default = 'stress')
- score_quan_thr
+- score_quan_thr
active subnetwork score quantile threshold. Must be
between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
- sig_gene_thr
+- sig_gene_thr
threshold for the minimum proportion of significant genes in
the subnetwork (Default = 0.02) If the number of genes to use as threshold is
calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number
is set to 2
- ...
+- ...
additional arguments for input_processing
Value
-
-
-a list of ggplot objects of graph visualizations of identified active
+
a list of ggplot objects of graph visualizations of identified active
subnetworks. Green nodes are down-regulated genes, reds are up-regulated genes
and yellows are non-input genes
@@ -182,16 +182,16 @@ Examples
-
+
-
+
-
+
Visualize Interactions of Genes Involved in the Given Enriched Terms
- Source: R/visualization.R
+ Source: R/visualization.R
visualize_term_interactions.Rd
@@ -88,27 +88,27 @@ Visualize Interactions of Genes Involved in the Given Enriched Terms
Arguments
- - result_df
+
+
+- result_df
Data frame of enrichment results. Must-have columns
are: 'Term_Description', 'Up_regulated' and 'Down_regulated'
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- show_legend
+- show_legend
Boolean to indicate whether to display the legend (TRUE)
or not (FALSE) (default: TRUE)
Value
-
-
-list of ggplot objects (named by Term ID) visualizing the interactions of genes involved
+
list of ggplot objects (named by Term ID) visualizing the interactions of genes involved
in the given enriched terms (annotated in the result_df) in the PIN used
for enrichment analysis (specified by pin_name_path).
@@ -131,10 +131,10 @@ See also
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
result_df <- example_pathfindR_output[1:2, ]
gg_list <- visualize_term_interactions(result_df, pin_name_path = 'IntAct')
-}
+} # }
@@ -149,16 +149,16 @@ Examples
-
+
-
+
-
+
Create Diagrams for Enriched Terms
- Source: R/visualization.R
+ Source: R/visualization.R
visualize_terms.Rd
@@ -94,30 +94,32 @@ Create Diagrams for Enriched Terms
Arguments
- - result_df
+
+
+- result_df
Data frame of enrichment results. Must-have columns for
KEGG human pathway diagrams (is_KEGG_result = TRUE) are: 'ID' and 'Term_Description'.
Must-have columns for the rest are: 'Term_Description', 'Up_regulated' and
'Down_regulated'
- input_processed
+- input_processed
input data processed via input_processing,
not necessary when is_KEGG_result = FALSE
- is_KEGG_result
+- is_KEGG_result
boolean to indicate whether KEGG gene sets were used for
enrichment analysis or not (default = TRUE)
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- ...
+- ...
additional arguments for visualize_KEGG_diagram (used
when is_KEGG_result = TRUE) or visualize_term_interactions
(used when is_KEGG_result = FALSE)
Arguments
Value
-
-
-Depending on the argument is_KEGG_result, creates visualization of
- interactions of genes involved in the list of enriched terms in
-
-result_df. Returns a list of ggplot objects named by Term ID.
+ Depending on the argument is_KEGG_result, creates visualization of
+ interactions of genes involved in the list of enriched terms in
+ result_df. Returns a list of ggplot objects named by Term ID.
Details
@@ -150,7 +149,7 @@ See also
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
input_processed <- data.frame(
GENE = c("PARP1", "NDUFA1", "STX6", "SNAP23"),
CHANGE = c(1.5, -2, 3, 5)
@@ -159,7 +158,7 @@ Examples
gg_list <- visualize_terms(result_df, input_processed)
gg_list2 <- visualize_terms(result_df, is_KEGG_result = FALSE, pin_name_path = 'IntAct')
-}
+} # }
@@ -174,16 +173,16 @@ Examples
-
+
pathfindR Analysis for non-Homo-sapiens organisms
Ege Ulgen
-2024-05-04
+2024-10-14
- Source:vignettes/non_hs_analysis.Rmd
+ Source: vignettes/non_hs_analysis.Rmd
non_hs_analysis.RmdBuilt-in Mus musculus Data - -
Site built with pkgdown 2.0.9.
+Site built with pkgdown 2.1.1.
- + - - +Obtaining PIN and Gene Sets Data
-2024-05-04
+2024-10-14
- Source:vignettes/obtain_data.Rmd
+ Source: vignettes/obtain_data.Rmd
obtain_data.RmdMSigDB Gene Sets - -
MSigDB Gene Sets
-Site built with pkgdown 2.0.9.
+Site built with pkgdown 2.1.1.
-
+
-
-
+
-
+
@@ -96,7 +95,7 @@
-
+
@@ -104,9 +103,9 @@
Visualization of pathfindR Enrichment
Results
- 2024-05-04
+ 2024-10-14
- Source: vignettes/visualization_vignette.Rmd
+ Source: vignettes/visualization_vignette.Rmd
visualization_vignette.Rmd
@@ -253,8 +252,9 @@
By default the node sizes are plotted proportional to the number of
genes a term contains (num_genes). To adjust node sizes
-using the \(-log_{10}\)(lowest p
-values), set node_size = "p_val":
+using the
+(lowest
+p values), set node_size = "p_val":
term_gene_graph(example_pathfindR_output, num_terms = 3, node_size = "p_val")
See ?term_gene_graph for more details.
@@ -300,9 +300,7 @@
+
@@ -315,17 +313,17 @@
-
+
-
+
-
+
@@ -77,7 +77,7 @@
Authors and Citation
-
+
-
+
Changelog • pathfindR
-
-
+
+
-
+
-
+
- - Version dev
- - Version 2.4
- - Version 2.3
- - Version 2.2
- - Version 2.1
- - Version 2.0
- - Version 1.6
- - Version 1.5
- - Version 1.4
- - Version 1.3
- - Version 1.2
- - Version 1.1
-
+
+
+
+pathfindR 2.4.12024-05-04
+
+Minor Changes and Bug Fixes
+- fixed a bug regarding KEGG gene set fetching: removed the conversion functionality in
get_kegg_gsets() which now returns KEGG IDs so that the user can convert the returned identifiers using a more appropriate tool (e.g. BioMart) should they wish
+
+
+
+pathfindR 2.4.02024-04-28
+
+Major Changes
+- implemented a new
color_kegg_pathway() function using ggkegg to create colored KEGG pathway ggplot objects (instead of using KEGGREST to obtain the colored PNG files, which no longer works #169)
+- renamed the
visualize_hsa_KEGG function to visualize_KEGG_diagram() to reflect this is now able to handle KEGG pathway enrichment results from any organism
+- updated the
visualize_terms(), visualize_term_interactions() and visualize_KEGG_diagram() functions so that they now return a list of ggplot objects (named by term ID)
+- updated the
get_kegg_gsets() function to also use ggkegg for fetching genes per pathway data
+- removed unneeded dependencies:
magick, KEGGgraph and KEGGREST
+
+
+
+Minor Changes and Bug Fixes
+- updated the
get_biogrid_pin() function so that it can now determine the latest version and download/process it from BioGRID (via setting release = "latest", which is now the default behavior)
+
+
+
+pathfindR 2.3.12024-01-19
+
+Minor Changes and Bug Fixes
+- fixed a bug in the
UpSet_plot() plot function regarding the interaction with ggupset package that was discovered in a reverse dependency check for ggplot2 3.5.0 (#189)
+- fixed gene symbol case mismatch issue in
score_terms() (#186)
+- applied enhancement suggestion from #184 to enable scale fill manual for
term_gene_graph()
+
+
+
+
+pathfindR 2.3.02023-10-08
+
+Major Changes
+- reverted removal of
create_HTML_report() so run_pathfindR() once again generates HTML reports
+
+
+
+pathfindR 2.2.02023-08-20
+
+Minor Changes and Bug Fixes
+- added the
disable_parallel argument in active_snw_enrichment_wrapper() to be able to disable parallel runs via foreach
+
+- fixed the issue encountered on CentOS where
forech wasn’t loading pathfindR (#164)
+- fixed a CRAN error due to a package documentation issue (#172)
+- performed some refactoring and updated/improved all tests
+
+
+
+pathfindR 2.1.02023-05-12
+
+Minor Changes and Bug Fixes
+- removed
create_HTML_report() so run_pathfindR() no longer generates a HTML report
+
+
+
+pathfindR 2.0.12023-05-10
+
+Minor Changes and Bug Fixes
+- added the
dir_for_report argument in the internal function create_HTML_report() to fix test issues on CRAN
+
+
+
+pathfindR 2.0.02023-05-06
+
+Major Changes
+- updated the java active subnetwork search component and added the
seedForRandom argument in active_snw_search()to ensure reproducibility. By default behavior, in run_pathfindR(), a seed is set for each iteration to produce reproducible results (#108)
+- as the example input/output data were renamed for convenience in ‘pathfindR.data’ v2.0, ‘pathfindR’ now depends on pathfindR.data (>= 2.0)
+- refactored/simplified
run_pathfindR()
+
+- visualization enriched term diagrams are now NOT part of
run_pathfindR()
+
+- default behavior of
run_pathfindR() is now to run in a temporary directory. The user can still set output_dir to run in a specified directory and also produce HTML reports
+- in
hierarchical_term_clustering(), update the sequence of number of clusters for which silhouette width is calculated for choosing the optimal number of clusters. This should speed up the function for cases with a large number of enriched terms
+- updated the relevant vignettes to reflect the implemented changes
+
+
+Minor Changes and Bug Fixes
+- fixed a minor issue in
return_pin_path() where the PIN was not properly read (#157)
+
+
+
+pathfindR 1.6.42022-08-25
+
+Minor Changes and Bug Fixes
+- updated the alias selection function within
input_processing() so that an alias that is not already present is selected
+- updated the min-max scaling (controlled by
scale_vals) in color_kegg_pathway(), the default is now scale_vals=TRUE
+
+- updated the
term_gene_heatmap() function so that legend title is shown and can be customized
+- updated the
term_gene_heatmap() function so that coloring is proper when no change values are provided in genes_df
+
+- added the
sort_terms_by_p argument to the term_gene_heatmap() function to enable sorting of terms by ‘lowest_p’
+- in visualization functions, made coloring of up-/down-regulated genes consistent (#126)
+- added the
vertex.label.cex and vertex.size.scaling arguments to cluster_graph_vis()
+
+- added the
show_legend argument to visualize_term_interactions() to toggle the legend
+
+
+
+pathfindR 1.6.32021-11-15
+
+Minor Changes and Bug Fixes
+- Fixed coloring issue in
color_kegg_pathway()
+
+- In
color_kegg_pathway() the default value for normalize_vals is now FALSE
+
+
+
+
+pathfindR 1.6.22021-08-21
+
+Major Changes
+- fixed an issue in
get_kegg_gsets() where empty result was returned for some organisms due to an error in parsing (#72)
+
+
+Minor Changes and Bug Fixes
+- added
repel = TRUE in term_gene_graph() and combined_results_graph() for better visualization of labels
+- fixed minor issue in
enrichment_chart() (#75)
+- fixed minor issue in
visualize_term_interactions()
+
+- fixed issue in
get_biogrid_pin() where the download method was set to wget (now set to auto, per #83)
+- updated to using tab3 format for
get_biogrid_pin() (if tab3 is available for the chosen release, otherwise tab2 format is used)
+- updated the default version of PIN obtained by
get_biogrid_pin() to ‘4.4.200’
+- in
get_kegg_gsets(), improved parsing of KEGG term descriptions so that no description is duplicated (#87)
+- in
score_terms(), if using descriptions, the ID is now appended for (any) duplicated term descriptions (#87)
+- in
obtain_colored_url(), swapped bg_color with fg_color due to an issue with KEGGREST
+
+- added legend to
term_gene_heatmap() (#95)
+- in
get_biogrid_pin(), the “download.file.method” from global options is used
+-
+
combined_results_graph() raises an error if there are no common terms in the combined data frame
+
+
+
+pathfindR 1.6.12021-01-04
+
+Major Changes
+- In
run_pathfindR(), the default iterations was set back to 10 (the default for all other v1.x)
+
+
+pathfindR 1.6.02020-11-21
+
+Major Changes
+- In
run_pathfindR(), as “GR” (the default active subnetwork search method) provides nearly identical results in each iteration, the default iterations is set to 1
+- added the column ‘support’ (the proportion of active subnetworks leading to enrichment over all subnetworks) in the output
+- updated the download URL in
get_biogrid_pin() as BioGRID updated the URL for download
+
+
+Minor Changes and Bug Fixes
+- changed old argument in the “Step-by-Step Execution of the pathfindR Enrichment Workflow” vignette
+- fixed an issue in
visualize_term_interactions() where the file name was too long, it was causing an error on Windows. Limited to 100 characters (#58)
+
+
+
+pathfindR 1.5.12020-09-20
+
+Minor Changes and Bug Fixes
+- Fixed issue in
check_java_version() where java version 14 could not be parsed (#49)
+- Fixed issue in
combined_results_graph() where gene nodes were not colored correctly (#55)
+
+
+
+pathfindR 1.5.02020-06-06
+
+Major Changes
+- created separate package
pathfindR.data for storing pathfindR data
+- added the function
visualize_active_subnetworks() for visualizing graphs of active subnetworks
+- add the new vignette “Comparing Two pathfindR Results” that briefly describes how different pathfindR results can be compared
+- added the functions
combine_pathfindR_results() and combined_results_graph() for comparison of 2 pathfindR results and term-gene graph of the combined results, respectively
+- added the function
get_pin_file() for obtaining organism-specific PIN data (only from BioGRID for now)
+- added the function
get_gene_sets_list() for obtaining organism-specific gene sets list from KEGG, Reactome and MSigDB
+- added the function
term_gene_heatmap() to create heatmap visualizations of enriched terms and the involved input genes. Rows are enriched terms and columns are involved input genes. If genes_df is provided, colors of the tiles indicate the change values
+- added the function
UpSet_plot() to create UpSet plots of enriched terms
+- added the human cell markers gene sets data
cell_markers_gsets and cell_markers_descriptions
+
+
+
+Minor Changes and Bug Fixes
+- fixed an issue regarding
parallel::makeCluster() in run_pathfindR() (#45)
+- fixed save-related issue in
download_kegg_png() (#37, @rix133)
+- added the output data
RA_comparison_output of pathfindR results on another RA-related dataset (GSE84074)
+- in
visualize_hsa_KEGG(), fixed the issue where >1 entrez ids were returned for a gene symbol (the first one is kept)
+- in
visualize_hsa_KEGG(), implemented a tryCatch to avoid any issues when KEGGREST::color.pathway.by.objects() might fail (#28)
+- in
visualize_hsa_KEGG(), now limiting the number of genes passes onto KEGGREST::color.pathway.by.objects() to < 60 (because the KEGG API now limits the number?)
+- changed default visualization in
term_gene_heatmap() (i.e. when genes_df is not provided) to binary colored heatmap (by default, “green” and “red”, controlled by low and high) by up-/down- regulation status
+- update the vignette “pathfindR Analysis for non-Homo-sapiens organisms” to reflect new data generation functions
get_pin_file() and get_gene_sets_list() and fixed a minor issue in the vignette (#46)
+
+
+
+pathfindR 1.4.22019-12-06
+
+Minor Changes and Bug Fixes
+- Fixed corner case in
create_kappa_matrix() when chance is 1, the metric is turned into 0
+- Fixed misused
class(.) == * in cluster_graph_vis()
+
+
+
+
+pathfindR 1.4.12019-11-15
+
+Major Changes
+- Fixed error in DESCRIPTION: the Java version in SystemRequirements was corrected to “Java (>= 8.0)”
+- The Java version is now checked
+
+
+Minor Changes and Bug Fixes
+- Fixed behavior: when no input genes are present in the enriched hsa KEGG pathway, visualization of the pathway is now skipped
+- Added the argument
max_to_plot to visualize_hsa_KEGG() and to run_pathfindR(). This argument controls the number of pathways to be visualized (default is NULL, i.e. no filter). This was implemented not to slow down the runtime of run_pathfindR() as downloading the png files is slow.
+- Fixed links to visualizations in
enriched_ters.Rmd
+
+
+
+
+pathfindR 1.4.02019-11-08
+
+Major Changes
+- Replaced most occurrences of “pathway” to “term”. This was adapted because “term” reflects the utility of the package better. The enrichment and clustering approaches work with any kind of gene set data (be it pathway gene sets, gene ontology gene sets, motif gene sets etc.) Accordingly:
+
-
+
DESCRIPTION was updated
+- The functions
annotate_pathway_DEGs(), calculate_pw_scores(), cluster_pathways(), fuzzy_pw_clustering(), hierarchical_pw_clustering(), visualize_pw_interactions() and visualize_pws() were renamed to annotate_term_DEGs(), score_terms(), cluster_enriched_terms(), fuzzy_term_clustering(), hierarchical_term_clustering(), visualize_term_interactions() and visualize_terms() respectively
+- The Rmd template file for the report
enriched_pathways.Rmd was renamed to enriched_terms.Rmd
+
+- All the Rmd template files for the report were updated
+- Documentation of each function was updated accordingly
+
+- Added the visualization function
term_gene_graph(), which creates a graph of enriched terms - involved genes
+- Made changes in
enrichment() and enrichment_analyses() to get enrichment results faster
+- Added the function
fetch_gene_set() for obtaining gene set data more easily
+- Terms in gene sets can now be filtered according to the number of genes a term contains (controlled by
min_gset_size, max_gset_size in fetch_gene_set() and run_pathfindR())
+- Added the argument
gaCrossover during active subnetwork search which controls the probability of a crossover in GA (default = 1, i.e. always perform crossover)
+- Added unit tests using
testthat
+
+- Updated all gene sets data
+- Updated all RA example data
+- The vignettes were updated
+- Updated all PIN data
+- Improved speed of kappa matrix calculation (
create_kappa_matrix())
+- Added vignette for non-Homo-sapiens organisms
+- Added Mus musculus (mmu) data:
+
-
+
mmu_kegg_genes & mmu_kegg_descriptions: mmu KEGG gene sets data
+- mmu STRING PIN
+-
+
myeloma_input & myeloma_output: example mmu input and output data
+
+- Added the STRING PIN (combined score >= 400)
+- The argument
sig_gene_thr in subnetwork filtering via filterActiveSnws() now serves the threshold proportion of significant genes in the active subnetwork. e.g., if there are 100 significant genes and sig_gene_thr = 0.03, subnetwork that contain at least 3 (100 x 0.03) significant genes will be accepted for further analysis
+- Removed
pathview dependency by implementing colored pathway diagram visualization function using KEGGREST and KEGGgraph
+
+
+
+Minor Changes and Bug Fixes
+- In
hierarchical_term_clustering(), redefined the distance measure as 1 - kappa statistic
+
+- Fixed minor issue in
cluster_graph_vis() (during the calculations for additional node colors)
+- Removed title from graph visualization of hierarchical clustering in
cluster_graph_vis()
+
+- In
active_snw_search(), unnecessary warnings during active subnetwork search were removed
+- Fixed minor issue in
enrichment_chart(), supplying fuzzy clustered results no longer raises an error
+- Added new checks in
input_testing() and input_processing() to ensure that both the initial input data frame and the processed input data frame for active subnetwork search contain at least 2 genes (to fix the corner case encountered in issue #17)
+- Fixed minor issue in
enrichment_chart(), ensuring that bubble sizes displayed in the legend (proportional to # of DEGs) are integers
+- In
enrichment_chart(), added the arguments num_bubbles (default is 4) to control number of bubbles displayed in the legend and even_breaks (default is TRUE) to indicate if even increments of breaks are required
+- Updated the logo
+- Minor fix in
term_gene_graph() (create the igraph object as an undirected graph for better auto layout)
+- Minor fix in
visualize_term_interactions(). The legend no longer displays “Non-input Active Snw. Genes” if they were not provided
+- The argument
human_genes in run_pathfindR() and input_processing() was renamed as convert2alias
+
+- The gene symbols in the input data frame, the PIN and the gene sets are now turned into uppercase (for obtaining the best overlap)
+- Added the argument
top_terms to enrichment_chart(), controlling the number top enriched terms to plot (default is 10)
+- Other minor bug/error fixes
+
+
+
+pathfindR 1.3.02018-11-20
+
+Major Changes
+- Separated the steps of the function
run_pathfindR into individual functions: active_snw_search, enrichment_analyses, summarize_enrichment_results, annotate_pathway_DEGs, visualize_pws.
+- renamed the function
pathmap as visualize_hsa_KEGG, updated the function to produce different visualizations for inputs with binary change values (ordered) and no change values (the input_processing function, assigns a change value of 100 to all).
+- Created new the visualization function
visualize_pw_interactions, which creates PNG files visualizing the interactions (in the selected PIN) of genes involved in the given pathways.
+- Added new vignette, describing the step-by-step execution of the pathfindR workflow
+- Changed clustering metric to kappa statistic, created the new clustering related functions
create_kappa_matrix, hierarchical_pw_clustering, fuzzy_pw_clustering and cluster_pathways.
+- Implemented the new function
cluster_graph_vis for visualizing graph diagrams of clustering results.
+
+
+Minor Changes and Bug Fixes
+- Fixed the bug where the arguments
score_quan_thr and sig_gene_thr for run_pathfindR were not being utilized.
+- in
run_pathfindR, added message at the end of run, reporting the number enriched pathways.
+- the function
run_pathfindR now creates a variable org_dir that is the “path/to/original/working/directory”. org_dir is used in multiple functions to return to the original working directory if anything fails. This changes the previous behavior where if a function stopped with an error the directory was changed to “..”, i.e. the parent directory. This change was adapted so that the user is returned to the original working directory if they supply a recursive output folder (output_dir, e.g. “./ALL_RESULTS/RESULT_A”).
+- in
input_processing, added the argument human_genes to only perform alias symbol conversion when human gene symbols are provided. - Updated the Rmd files used to create the report HTML files
+- Added the data for
GO-All, all annotations in the GO database (BP+MF+CC)
+- Updated the vignette
pathfindR - An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworks to reflect the new functionalities.
+
+
+
+pathfindR 1.2.32018-10-26
+
+Minor Changes and Bug Fixes
+- in the function
plot_scores, added the argument label_cases to indicate whether or not to label the cases in the pathway scoring heatmap plot. Also added the argument case_control_titles which allows the user to change the default “Case” and “Control” headers. Also added the arguments low and high used to change the low and high end colors of the scoring color gradient.
+- in the function
plot_scores, reversed the color gradient to match the coloring scheme used by pathview (i.e. red for positive values, green for negative values)
+- minor change in
parseActiveSnwSearch, replaced score_thr by score_quan_thr. This was done so that the scoring filter for active subnetworks could be performed based on the distribution of the current active subnetworks and not using a constant empirical score value threshold.
+- minor change in
parseActiveSnwSearch, increased sig_gene_thr from 2 to 10 as we observed in most of the cases, this resulted in faster runs with comparable results.
+- in
choose_clusters, added the argument p_val_threshold to be used as p value threshold for filtering the enriched pathways prior to clustering.
+
+
+
+
+pathfindR 1.2.12018-08-17
+
+Major Changes
+- Added the option to specify a custom gene set when using
run_pathfindR. For this, the gene_sets argument should be set to “Custom” and custom_genes and custom_pathways should be provided.
+
+
+Minor Changes and Bug Fixes
+- fixed minor bug in
calculate_pw_scores where if there was one DEG, subsetting the experiment matrix failed
+- added if condition to check if there were DEGs in
calculate_pw_scores. If there is none, the pathway is skipped.
+- in
calculate_pw_scores, if cases are provided, the pathways are reordered before plotting the heat map and returning the matrix according to their activity in cases. This way, “up” pathways are grouped together, same for “down” pathways.
+- in
calculate_pwd, if a pathway has perfect overlap with other pathways, change the correlation value with 1 instead of NA.
+- in
choose_clusters, if result_df has less than 3 pathways, do not perform clustering.
+-
+
run_pathfindR checks whether the output directory (output_dir) already exists and if it exists, now appends “(1)” to output_dir and displays a warning message. This was implemented to prevent writing over existing results.
+- in run
run_pathfindR, recursive creation for the output directory (output_dir) is now supported.
+- in run
run_pathfindR, if no pathways are found, the function returns an empty data frame instead of raising an error.
+
+
+
+pathfindR 1.2
+
+Major Changes
+Implemented the (per subject) pathway scoring function calculate_pw_scores and the function to plot the heatmap of pathway scores per subject plot_scores.
Added the auto parameter to choose_clusters. When auto == TRUE (default), the function chooses the optimal number of clusters k automatically, as the value which maximizes the average silhouette width. It then returns a data frame with the cluster assignments and the representative/member statuses of each pathway.
Added the Fold_Enrichment column to the resulting data frame of enrichment, and as a corollary to the resulting data frame of run_pathfindR.
Added the option bubble to plot a bubble chart displaying the enrichment results in run_pathfindR using the helper function enrichment_chart. To plot the bubble chart set bubble = TRUE in run_pathfindR or use enrichment_chart(your_result_df).
+
+Minor Changes and Bug Fixes
+Add the parameter silent_option to run_pathfindR. When silent_option == TRUE (default), the console outputs during active subnetwork search are printed to a file named “console_out.txt”. If silent_option == FALSE, the output is printed on the screen. Default was set to TRUE because multiple console outputs are simultaneously printed when running in parallel.
Added the list_active_snw_genes parameter to run_pathfindR. When list_active_snw_genes == TRUE, the function adds the column non_DEG_Active_Snw_Genes, which reports the non-DEG active subnetwork genes for the active subnetwork which was enriched for the given pathway with the lowest p value.
Added the data RA_clustered, which is the example output of the clustering workflow.
In the function, run_pathfindR added the option to specify the argument output_dir which specifies the directory to be created under the current working directory for storing the result HTML files. output_dir is “pathfindR_Results” by default.
run_pathfindR now checks whether the output directory (output_dir) already exists and if it exists, stops and displays an error message. This was implemented to prevent writing over existing results.
genes_table.html now contains a second table displaying the input gene symbols for which there were no interactions in the PIN.
+
+
+pathfindR 1.1
+
+Major changes
+- Added the
gene_sets option in run_pathfindR to chose between different gene sets. Available gene sets are KEGG, Reactome, BioCarta and Gene Ontology gene sets (GO-BP, GO-CC and GO-MF)
+-
+
cluster_pathways automatically recognizes the ID type and chooses the gene sets accordingly
+
+
+Minor Changes and Bug Fixes
+- Fixed issue regarding p values < 1e-13. No active subnetworks were found when there were p values < 1e-13. These are now changed to 1e-13 in the function
input_processing
+
+- In
input_processing, genes for which no interactions are found in the PIN are now removed before active subnetwork search
+- Duplicated gene symbols no longer raise an error. If there are duplicated symbols, the lowest p value is chosen for each gene symbol in the function
input_processing
+
+- To prevent the formation of nested folders, by default and on errors, the function
run_pathfindR returns to the user’s working directory.
+- Citation information are now provided for our BioRxiv pre-print
+
+
+
+
+
+
-
+
-
+
-
+
Create UpSet Plot of Enriched Terms
- Source: R/visualization.R
+ Source: R/visualization.R
UpSet_plot.Rd
@@ -98,7 +98,9 @@ Create UpSet Plot of Enriched Terms
Arguments
- - result_df
+
+
+- result_df
A dataframe of pathfindR results that must contain the following columns:
- Term_Description
Description of the enriched term (necessary if use_description = TRUE)
Arguments
- genes_df
+- genes_df
the input data that was used with run_pathfindR.
It must be a data frame with 3 columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (optional)
Arguments
The change values in this data frame are used to color the affected genes
- num_terms
+- num_terms
Number of top enriched terms to use while creating the plot. Set to NULL to use
all enriched terms (default = 10)
- method
+- method
the option for producing the plot. Options include 'heatmap',
'boxplot' and 'barplot'. (default = 'heatmap')
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- low
+- low
a string indicating the color of 'low' values in the coloring gradient (default = 'green')
- mid
+- mid
a string indicating the color of 'mid' values in the coloring gradient (default = 'black')
- high
+- high
a string indicating the color of 'high' values in the coloring gradient (default = 'red')
- ...
+- ...
additional arguments for input_processing (used if
genes_df is provided)
Value
-
-
-UpSet plots are plots of the intersections of sets as a matrix. This
+
UpSet plots are plots of the intersections of sets as a matrix. This
function creates a ggplot object of an UpSet plot where the x-axis is the
UpSet plot of intersections of enriched terms. By default (i.e.
method = 'heatmap') the main plot is a heatmap of genes at the
@@ -192,16 +192,16 @@
Examples
-
+
-
+
-
+
Wrapper for Active Subnetwork Search + Enrichment over Single/Multiple Iteration(s)
- Source: R/utility.R
+ Source: R/utility.R
active_snw_enrichment_wrapper.Rd
@@ -115,122 +115,124 @@ Wrapper for Active Subnetwork Search + Enrichment over Single/Multiple Itera
Arguments
- - input_processed
+
+
+- input_processed
processed input data frame
- pin_path
+- pin_path
path/to/PIN/file
- gset_list
+- gset_list
list for gene sets
- enrichment_threshold
+- enrichment_threshold
adjusted-p value threshold used when filtering
enrichment results (default = 0.05)
- list_active_snw_genes
+- list_active_snw_genes
boolean value indicating whether or not to report
the non-significant active subnetwork genes for the active subnetwork which was enriched for
the given term with the lowest p value (default = FALSE)
- adj_method
+- adj_method
correction method to be used for adjusting p-values.
(default = 'bonferroni')
- search_method
+- search_method
algorithm to use when performing active subnetwork
search. Options are greedy search (GR), simulated annealing (SA) or genetic
algorithm (GA) for the search (default = 'GR').
- disable_parallel
+- disable_parallel
boolean to indicate whether to disable parallel runs
via foreach (default = FALSE)
- use_all_positives
+- use_all_positives
if TRUE: in GA, adds an individual with all positive
nodes. In SA, initializes candidate solution with all positive nodes. (default = FALSE)
- iterations
+- iterations
number of iterations for active subnetwork search and
enrichment analyses (Default = 10)
- n_processes
+- n_processes
optional argument for specifying the number of processes
used by foreach. If not specified, the function determines this
automatically (Default == NULL. Gets set to 1 for Genetic Algorithm)
- score_quan_thr
+- score_quan_thr
active subnetwork score quantile threshold. Must be
between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
- sig_gene_thr
+- sig_gene_thr
threshold for the minimum proportion of significant genes in
the subnetwork (Default = 0.02) If the number of genes to use as threshold is
calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number
is set to 2
- saTemp0
+- saTemp0
Initial temperature for SA (default = 1.0)
- saTemp1
+- saTemp1
Final temperature for SA (default = 0.01)
- saIter
+- saIter
Iteration number for SA (default = 10000)
- gaPop
+- gaPop
Population size for GA (default = 400)
- gaIter
+- gaIter
Iteration number for GA (default = 200)
- gaThread
+- gaThread
Number of threads to be used in GA (default = 5)
- gaCrossover
+- gaCrossover
Applies crossover with the given probability in GA (default = 1, i.e. always perform crossover)
- gaMut
+- gaMut
For GA, applies mutation with given mutation rate (default = 0, i.e. mutation off)
- grMaxDepth
+- grMaxDepth
Sets max depth in greedy search, 0 for no limit (default = 1)
- grSearchDepth
+- grSearchDepth
Search depth in greedy search (default = 1)
- grOverlap
+- grOverlap
Overlap threshold for results of greedy search (default = 0.5)
- grSubNum
+- grSubNum
Number of subnetworks to be presented in the results (default = 1000)
- silent_option
+- silent_option
boolean value indicating whether to print the messages
to the console (FALSE) or not (TRUE, this will print to a temp. file) during
active subnetwork search (default = TRUE). This option was added because
@@ -239,9 +241,7 @@
Arguments
Value
-
-
-Data frame of combined pathfindR enrichment results
+ Data frame of combined pathfindR enrichment results
@@ -256,16 +256,16 @@ Value
-
+
-
+
-
+
Perform Active Subnetwork Search
- Source: R/active_snw_search.R
+ Source: R/active_snw_search.R
active_snw_search.Rd
@@ -112,7 +112,9 @@ Perform Active Subnetwork Search
Arguments
- - input_for_search
+
+
+- input_for_search
input the input data that active subnetwork search uses. The input
must be a data frame containing at least these 2 columns:
- GENE
Gene Symbol
Arguments
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- snws_file
+- snws_file
name for active subnetwork search output data
without file extension (default = 'active_snws')
- dir_for_parallel_run
+- dir_for_parallel_run
(previously created) directory for a parallel run iteration.
Used in the wrapper function (see ?run_pathfindR) (Default = NULL)
- score_quan_thr
+- score_quan_thr
active subnetwork score quantile threshold. Must be
between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
- sig_gene_thr
+- sig_gene_thr
threshold for the minimum proportion of significant genes in
the subnetwork (Default = 0.02) If the number of genes to use as threshold is
calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number
is set to 2
- search_method
+- search_method
algorithm to use when performing active subnetwork
search. Options are greedy search (GR), simulated annealing (SA) or genetic
algorithm (GA) for the search (default = 'GR').
- seedForRandom
+- seedForRandom
seed for reproducibility while running the java modules (applies for GR and SA)
- silent_option
+- silent_option
boolean value indicating whether to print the messages
to the console (FALSE) or not (TRUE, this will print to a temp. file) during
active subnetwork search (default = TRUE). This option was added because
during parallel runs, the console messages get disorderly printed.
- use_all_positives
+- use_all_positives
if TRUE: in GA, adds an individual with all positive
nodes. In SA, initializes candidate solution with all positive nodes. (default = FALSE)
- geneInitProbs
+- geneInitProbs
For SA and GA, probability of adding a gene in initial solution (default = 0.1)
- saTemp0
+- saTemp0
Initial temperature for SA (default = 1.0)
- saTemp1
+- saTemp1
Final temperature for SA (default = 0.01)
- saIter
+- saIter
Iteration number for SA (default = 10000)
- gaPop
+- gaPop
Population size for GA (default = 400)
- gaIter
+- gaIter
Iteration number for GA (default = 200)
- gaThread
+- gaThread
Number of threads to be used in GA (default = 5)
- gaCrossover
+- gaCrossover
Applies crossover with the given probability in GA (default = 1, i.e. always perform crossover)
- gaMut
+- gaMut
For GA, applies mutation with given mutation rate (default = 0, i.e. mutation off)
- grMaxDepth
+- grMaxDepth
Sets max depth in greedy search, 0 for no limit (default = 1)
- grSearchDepth
+- grSearchDepth
Search depth in greedy search (default = 1)
- grOverlap
+- grOverlap
Overlap threshold for results of greedy search (default = 0.5)
- grSubNum
+- grSubNum
Number of subnetworks to be presented in the results (default = 1000)
Value
-
-
-A list of genes in every identified active subnetwork that has a score greater than
+
A list of genes in every identified active subnetwork that has a score greater than
the `score_quan_thr`th quantile and that has at least `sig_gene_thr` affected genes.
@@ -262,16 +262,16 @@ Examples
-
+
-
+
-
+
Annotate the Affected Genes in the Provided Enriched Terms
- Source: R/utility.R
+ Source: R/utility.R
annotate_term_genes.Rd
@@ -92,25 +92,25 @@ Annotate the Affected Genes in the Provided Enriched Terms
Arguments
- - result_df
+
+
+- result_df
data frame of enrichment results.
The only must-have column is 'ID'.
- input_processed
+- input_processed
input data processed via input_processing
- genes_by_term
+- genes_by_term
List that contains genes for each gene set. Names of
this list are gene set IDs (default = kegg_genes)
Value
-
-
-The original data frame with two additional columns:
- Up_regulated
+ The original data frame with two additional columns:
- Up_regulated
the up-regulated genes in the input involved in the given term's gene set, comma-separated
- Down_regulated
@@ -143,16 +143,16 @@ Examples
-
+
-
+
-
+
@@ -88,16 +88,16 @@ Check Java Version
Arguments
- - version
+
+
+- version
character vector containing the output of 'java -version'. If
NULL, result of fetch_java_version is used (default = NULL)
Value
-
-
-only parses and checks whether the java version is >= 1.8
+ only parses and checks whether the java version is >= 1.8
Details
@@ -116,16 +116,16 @@ Details
-
+
-
+
-
+
@@ -95,7 +95,9 @@ Cluster Enriched Terms
Arguments
- - enrichment_res
+
+
+- enrichment_res
data frame of pathfindR enrichment results. Must-have
columns are 'Term_Description' (if use_description = TRUE) or 'ID'
(if use_description = FALSE), 'Down_regulated', and 'Up_regulated'.
@@ -103,29 +105,29 @@
Arguments
provided.- method
+- method
Either 'hierarchical' or 'fuzzy'. Details of clustering are
provided in the corresponding functions hierarchical_term_clustering,
and fuzzy_term_clustering
- plot_clusters_graph
+- plot_clusters_graph
boolean value indicate whether or not to plot
the graph diagram of clustering results (default = TRUE)
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- use_active_snw_genes
+- use_active_snw_genes
boolean to indicate whether or not to use
non-input active subnetwork genes in the calculation of kappa statistics
(default = FALSE, i.e. only use affected genes)
- ...
+- ...
additional arguments for hierarchical_term_clustering,
fuzzy_term_clustering and cluster_graph_vis.
See documentation of these functions for more details.
Arguments
Value
-
-
-a data frame of clustering results. For 'hierarchical', the cluster
+
a data frame of clustering results. For 'hierarchical', the cluster
assignments (Cluster) and whether the term is representative of its cluster
(Status) is added as columns. For 'fuzzy', terms that are in multiple
clusters are provided for each cluster. The cluster assignments (Cluster)
@@ -177,16 +177,16 @@
Examples
-
+
-
+
-
+
Graph Visualization of Clustered Enriched Terms
- Source: R/clustering.R
+ Source: R/clustering.R
cluster_graph_vis.Rd
@@ -96,17 +96,19 @@ Graph Visualization of Clustered Enriched Terms
Arguments
- - clu_obj
+
+
+- clu_obj
clustering result (either a matrix obtained via
hierarchical_term_clustering or fuzzy_term_clustering
`fuzzy_term_clustering` or a vector obtained via `hierarchical_term_clustering`)
- kappa_mat
+- kappa_mat
matrix of kappa statistics (output of create_kappa_matrix)
- enrichment_res
+- enrichment_res
data frame of pathfindR enrichment results. Must-have
columns are 'Term_Description' (if use_description = TRUE) or 'ID'
(if use_description = FALSE), 'Down_regulated', and 'Up_regulated'.
@@ -114,29 +116,27 @@
Arguments
provided.- kappa_threshold
+- kappa_threshold
threshold for kappa statistics, defining strong
relation (default = 0.35)
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- vertex.label.cex
+- vertex.label.cex
font size for vertex labels; it is interpreted as a multiplication factor of some device-dependent base font size (default = 0.7)
- vertex.size.scaling
+- vertex.size.scaling
scaling factor for the node size (default = 2.5)
Value
-
-
-Plots a graph diagram of clustering results. Each node is an enriched term
+
Plots a graph diagram of clustering results. Each node is an enriched term
from `enrichment_res`. Size of node corresponds to -log(lowest_p). Thickness
of the edges between nodes correspond to the kappa statistic between the two
terms. Color of each node corresponds to distinct clusters. For fuzzy
@@ -145,9 +145,9 @@
Value
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
cluster_graph_vis(clu_obj, kappa_mat, enrichment_res)
-}
+} # }
@@ -162,16 +162,16 @@ Examples
-
+
-
+
-
+
Color hsa KEGG pathway
- Source: R/visualization.R
+ Source: R/visualization.R
color_kegg_pathway.Rd
@@ -94,19 +94,21 @@ Color hsa KEGG pathway
Arguments
- - pw_id
+
+
+- pw_id
hsa KEGG pathway id (e.g. hsa05012)
- change_vec
+- change_vec
vector of change values, names should be hsa KEGG gene ids
- scale_vals
+- scale_vals
should change values be scaled? (default = TRUE)
- node_cols
+- node_cols
low, middle and high color values for coloring the pathway nodes
(default = NULL). If node_cols=NULL, the low, middle and high color
are set as 'green', 'gray' and 'red'. If all change values are 1e6 (in case no
@@ -114,26 +116,24 @@
Arguments
input_processing), only one color ('#F38F18' if NULL) is used.- legend.position
+- legend.position
the default position of legends ("none", "left",
"right", "bottom", "top", "inside")
Value
-
-
-a ggplot object containing the colored KEGG pathway diagram visualization
+ a ggplot object containing the colored KEGG pathway diagram visualization
Examples
-
@@ -148,16 +148,16 @@ Examples
-
+
-
+
-
+
Combine 2 pathfindR Results
- Source: R/comparison.R
+ Source: R/comparison.R
combine_pathfindR_results.Rd
@@ -88,24 +88,24 @@ Combine 2 pathfindR Results
Arguments
-
Value
-
-
-Data frame of combined pathfindR enrichment results. Columns are:
- ID
+ Data frame of combined pathfindR enrichment results. Columns are:
- ID
ID of the enriched term
- Term_Description
@@ -160,8 +160,8 @@ Value
Examples
combined_results <- combine_pathfindR_results(example_pathfindR_output, example_comparison_output)
-#> You may run `combined_results_graph()` to create visualizations of combined term-gene graphs of selected terms
+#> You may run `combined_results_graph()` to create visualizations of combined term-gene graphs of selected terms
@@ -176,16 +176,16 @@ Examples
-
+
-
+
-
+
@@ -94,35 +94,35 @@ Combined Results Graph
Arguments
- - combined_df
+
+
+- combined_df
Data frame of combined pathfindR enrichment results
- selected_terms
+- selected_terms
the vector of selected terms for creating the graph
(either IDs or term descriptions). If set to 'common', all of the
common terms are used. (default = 'common')
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- layout
+- layout
The type of layout to create (see ggraph for details. Default = 'stress')
- node_size
+- node_size
Argument to indicate whether to use number of significant genes ('num_genes')
or the -log10(lowest p value) ('p_val') for adjusting the node sizes (default = 'num_genes')
Value
-
-
-a ggraph object containing the combined term-gene graph.
+
a ggraph object containing the combined term-gene graph.
Each node corresponds to an enriched term (orange if common, different shades of blue otherwise),
an up-regulated gene (green), a down-regulated gene (red) or
a conflicting (i.e. up in one analysis, down in the other or vice versa) gene
@@ -155,16 +155,16 @@
Examples
-
+
-
+
-
+
@@ -88,16 +88,16 @@ Configure Output Directory Name
Arguments
-
Value
-
-
-/path/to/output/dir
+ /path/to/output/dir
@@ -112,16 +112,16 @@ Value
-
+
-
+
-
+
Create HTML Report of pathfindR Results
- Source: R/utility.R
+ Source: R/utility.R
create_HTML_report.Rd
@@ -88,7 +88,9 @@ Create HTML Report of pathfindR Results
Arguments
- - input
+
+
+- input
the input data that pathfindR uses. The input must be a data
frame with three columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (OPTIONAL)
Arguments
- input_processed
+- input_processed
processed input data frame
- final_res
+- final_res
final pathfindR result data frame
- dir_for_report
+- dir_for_report
directory to render the report in
@@ -121,16 +123,16 @@ Arguments
-
+
-
+
-
+
Create Kappa Statistics Matrix
- Source: R/clustering.R
+ Source: R/clustering.R
create_kappa_matrix.Rd
@@ -92,7 +92,9 @@ Create Kappa Statistics Matrix
Arguments
- - enrichment_res
+
+
+- enrichment_res
data frame of pathfindR enrichment results. Must-have
columns are 'Term_Description' (if use_description = TRUE) or 'ID'
(if use_description = FALSE), 'Down_regulated', and 'Up_regulated'.
@@ -100,12 +102,12 @@
Arguments
provided.- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- use_active_snw_genes
+- use_active_snw_genes
boolean to indicate whether or not to use
non-input active subnetwork genes in the calculation of kappa statistics
(default = FALSE, i.e. only use affected genes)
Arguments
Value
-
-
-a matrix of kappa statistics between each term in the
+
a matrix of kappa statistics between each term in the
enrichment results.
@@ -141,16 +141,16 @@ Examples
-
+
-
+
-
+
Perform Enrichment Analysis for a Single Gene Set
- Source: R/enrichment.R
+ Source: R/enrichment.R
enrichment.Rd
@@ -96,38 +96,40 @@ Perform Enrichment Analysis for a Single Gene Set
Arguments
- - input_genes
+
+
+- input_genes
The set of gene symbols to be used for enrichment
analysis. In the scope of this package, these are genes that were
identified for an active subnetwork
- genes_by_term
+- genes_by_term
List that contains genes for each gene set. Names of
this list are gene set IDs (default = kegg_genes)
- term_descriptions
+- term_descriptions
Vector that contains term descriptions for the
gene sets. Names of this vector are gene set IDs (default = kegg_descriptions)
- adj_method
+- adj_method
correction method to be used for adjusting p-values.
(default = 'bonferroni')
- enrichment_threshold
+- enrichment_threshold
adjusted-p value threshold used when filtering
enrichment results (default = 0.05)
- sig_genes_vec
+- sig_genes_vec
vector of significant gene symbols. In the scope of this
package, these are the input genes that were used for active subnetwork search
- background_genes
+- background_genes
vector of background genes. In the scope of this package,
the background genes are taken as all genes in the PIN
(see enrichment_analyses)
Arguments
Value
-
-
-A data frame that contains enrichment results
+ A data frame that contains enrichment results
See also
@@ -174,16 +174,16 @@ Examples
-
+
-
+
-
+
Perform Enrichment Analyses on the Input Subnetworks
- Source: R/enrichment.R
+ Source: R/enrichment.R
enrichment_analyses.Rd
@@ -97,42 +97,44 @@ Perform Enrichment Analyses on the Input Subnetworks
Arguments
- - snws
+
+
+- snws
a list of subnetwork genes (i.e., vectors of genes for each subnetwork)
- sig_genes_vec
+- sig_genes_vec
vector of significant gene symbols. In the scope of this
package, these are the input genes that were used for active subnetwork search
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- genes_by_term
+- genes_by_term
List that contains genes for each gene set. Names of
this list are gene set IDs (default = kegg_genes)
- term_descriptions
+- term_descriptions
Vector that contains term descriptions for the
gene sets. Names of this vector are gene set IDs (default = kegg_descriptions)
- adj_method
+- adj_method
correction method to be used for adjusting p-values.
(default = 'bonferroni')
- enrichment_threshold
+- enrichment_threshold
adjusted-p value threshold used when filtering
enrichment results (default = 0.05)
- list_active_snw_genes
+- list_active_snw_genes
boolean value indicating whether or not to report
the non-significant active subnetwork genes for the active subnetwork which was enriched for
the given term with the lowest p value (default = FALSE)
Arguments
Value
-
-
-a dataframe of combined enrichment results. Columns are:
- ID
+ a dataframe of combined enrichment results. Columns are:
- ID
ID of the enriched term
- Term_Description
@@ -191,16 +191,16 @@ Examples
-
+
-
+
-
+
Create Bubble Chart of Enrichment Results
- Source: R/visualization.R
+ Source: R/visualization.R
enrichment_chart.Rd
@@ -96,7 +96,9 @@ Create Bubble Chart of Enrichment Results
Arguments
- - result_df
+
+
+- result_df
a data frame that must contain the following columns:
- Term_Description
Description of the enriched term
Arguments
- top_terms
+- top_terms
number of top terms (according to the 'lowest_p' column)
to plot (default = 10). If plot_by_cluster = TRUE, selects the top
top_terms terms per each cluster. Set top_terms = NULL to plot
@@ -127,18 +129,18 @@
Arguments
all terms are plotted.- plot_by_cluster
+- plot_by_cluster
boolean value indicating whether or not to group the
enriched terms by cluster (works if result_df contains a
'Cluster' column).
- num_bubbles
+- num_bubbles
number of sizes displayed in the legend # genes
(Default = 4)
- even_breaks
+- even_breaks
whether or not to set even breaks for the number of sizes
displayed in the legend # genes. If TRUE (default), sets
equal breaks and the number of displayed bubbles may be different than the
@@ -148,9 +150,7 @@
Arguments
Value
-
-
-a ggplot2 object containing the bubble chart.
+
a ggplot2 object containing the bubble chart.
The x-axis corresponds to fold enrichment values while the y-axis indicates
the enriched terms. Size of the bubble indicates the number of significant
genes in the given enriched term. Color indicates the -log10(lowest-p) value.
@@ -177,16 +177,16 @@
Examples
-
+
-
+
-
+
@@ -96,7 +96,9 @@ Fetch Gene Set Objects
Arguments
- - gene_sets
+
+
+- gene_sets
Name of the gene sets to be used for enrichment analysis.
Available gene sets are 'KEGG', 'Reactome', 'BioCarta', 'GO-All',
'GO-BP', 'GO-CC', 'GO-MF', 'cell_markers', 'mmu_KEGG' or 'Custom'.
@@ -104,21 +106,21 @@
Arguments
must be specified. (Default = 'KEGG')- min_gset_size
+- min_gset_size
minimum number of genes a term must contain (default = 10)
- max_gset_size
+- max_gset_size
maximum number of genes a term must contain (default = 300)
- custom_genes
+- custom_genes
a list containing the genes involved in each custom
term. Each element is a vector of gene symbols located in the given custom
term. Names should correspond to the IDs of the custom terms.
- custom_descriptions
+- custom_descriptions
A vector containing the descriptions for each
custom term. Names of the vector should correspond to the IDs of the custom
terms.
Arguments
Value
-
-
-a list containing 2 elements
- genes_by_term
+ a list containing 2 elements
- genes_by_term
list of vectors of genes contained in each term
- term_descriptions
@@ -155,16 +155,16 @@ Examples
-
+
-
+
-
+
@@ -88,9 +88,7 @@ Obtain Java Version
Value
-
-
-character vector containing the output of 'java -version'
+ character vector containing the output of 'java -version'
Details
@@ -109,16 +107,16 @@ Details
-
+
-
+
-
+
Parse Active Subnetwork Search Output File and Filter the Subnetworks
- Source: R/active_snw_search.R
+ Source: R/active_snw_search.R
filterActiveSnws.Rd
@@ -93,21 +93,23 @@ Parse Active Subnetwork Search Output File and Filter the Subnetworks
Arguments
- - active_snw_path
+
+
+- active_snw_path
path to the output of an Active Subnetwork Search
- sig_genes_vec
+- sig_genes_vec
vector of significant gene symbols. In the scope of this
package, these are the input genes that were used for active subnetwork search
- score_quan_thr
+- score_quan_thr
active subnetwork score quantile threshold. Must be
between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
- sig_gene_thr
+- sig_gene_thr
threshold for the minimum proportion of significant genes in
the subnetwork (Default = 0.02) If the number of genes to use as threshold is
calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number
@@ -116,9 +118,7 @@
Arguments
Value
-
-
-A list containing subnetworks: a list of of genes in every
+
A list containing subnetworks: a list of of genes in every
active subnetwork that has a score greater than the score_quan_thrth
quantile and that contains at least sig_gene_thr of significant genes
and scores the score of each filtered active subnetwork
@@ -153,16 +153,16 @@ Examples
-
+
-
+
-
+
Heuristic Fuzzy Multiple-linkage Partitioning of Enriched Terms
- Source: R/clustering.R
+ Source: R/clustering.R
fuzzy_term_clustering.Rd
@@ -93,11 +93,13 @@ Heuristic Fuzzy Multiple-linkage Partitioning of Enriched Terms
Arguments
- - kappa_mat
+
+
+- kappa_mat
matrix of kappa statistics (output of create_kappa_matrix)
- enrichment_res
+- enrichment_res
data frame of pathfindR enrichment results. Must-have
columns are 'Term_Description' (if use_description = TRUE) or 'ID'
(if use_description = FALSE), 'Down_regulated', and 'Up_regulated'.
@@ -105,21 +107,19 @@
Arguments
provided.- kappa_threshold
+- kappa_threshold
threshold for kappa statistics, defining strong
relation (default = 0.35)
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
Value
-
-
-a boolean matrix of cluster assignments. Each row corresponds to an
+
a boolean matrix of cluster assignments. Each row corresponds to an
enriched term, each column corresponds to a cluster.
@@ -132,10 +132,10 @@ Details
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
fuzzy_term_clustering(kappa_mat, enrichment_res)
fuzzy_term_clustering(kappa_mat, enrichment_res, kappa_threshold = 0.45)
-}
+} # }
@@ -150,16 +150,16 @@ Examples
-
+
-
+
-
+
Retrieve the Requested Release of Organism-specific BioGRID PIN
- Source: R/data_generation.R
+ Source: R/data_generation.R
get_biogrid_pin.Rd
@@ -88,27 +88,27 @@ Retrieve the Requested Release of Organism-specific BioGRID PIN
Arguments
- - org
+
+
+- org
organism name. BioGRID naming requires underscores for spaces so
'Homo sapiens' becomes 'Homo_sapiens', 'Mus musculus' becomes 'Mus_musculus'
etc. See https://wiki.thebiogrid.org/doku.php/statistics for a full
list of available organisms (default = 'Homo_sapiens')
- path2pin
+- path2pin
the path of the file to save the PIN data. By default, the
PIN data is saved in a temporary file
- release
+- release
the requested BioGRID release (default = 'latest')
Value
-
-
-the path of the file in which the PIN data was saved. If
+
the path of the file in which the PIN data was saved. If
path2pin was not supplied by the user, the PIN data is saved in a
temporary file
@@ -125,16 +125,16 @@ Value
-
+
-
+
-
+
Retrieve Organism-specific Gene Sets List
- Source: R/data_generation.R
+ Source: R/data_generation.R
get_gene_sets_list.Rd
@@ -94,35 +94,35 @@ Retrieve Organism-specific Gene Sets List
Arguments
- - source
+
+
+- source
As of this version, either 'KEGG', 'Reactome' or 'MSigDB' (default = 'KEGG')
- org_code
+- org_code
(Used for 'KEGG' only) KEGG organism code for the selected organism. For a full list
of all available organisms, see https://www.genome.jp/kegg/catalog/org_list.html
- species
+- species
(Used for 'MSigDB' only) species name, such as Homo sapiens, Mus musculus, etc.
See msigdbr_show_species for all the species available in
the msigdbr package (default = 'Homo sapiens')
- collection
+- collection
(Used for 'MSigDB' only) collection. i.e., H, C1, C2, C3, C4, C5, C6, C7.
- subcollection
+- subcollection
(Used for 'MSigDB' only) sub-collection, such as CGP, MIR, BP, etc. (default = NULL,
i.e. list all gene sets in collection)
Value
-
-
-A list containing 2 elements:
gene_sets - A list containing the genes involved in each gene set
A list containing 2 elements:
gene_sets - A list containing the genes involved in each gene set
descriptions - A named vector containing the descriptions for each gene set
. For 'KEGG' and 'MSigDB', it is possible to choose a specific organism. For a full list
of all available KEGG organisms, see https://www.genome.jp/kegg/catalog/org_list.html.
@@ -143,16 +143,16 @@
Value
-
+
-
+
-
+
Retrieve Organism-specific KEGG Pathway Gene Sets
- Source: R/data_generation.R
+ Source: R/data_generation.R
get_kegg_gsets.Rd
@@ -88,16 +88,16 @@ Retrieve Organism-specific KEGG Pathway Gene Sets
Arguments
- - org_code
+
+
+- org_code
KEGG organism code for the selected organism. For a full list
of all available organisms, see https://www.genome.jp/kegg/catalog/org_list.html
Value
-
-
-list containing 2 elements:
gene_sets - A list containing KEGG IDs for the genes involved in each KEGG pathway
list containing 2 elements:
gene_sets - A list containing KEGG IDs for the genes involved in each KEGG pathway
descriptions - A named vector containing the descriptions for each KEGG pathway
@@ -113,16 +113,16 @@ Value
-
+
-
+
-
+
Retrieve Organism-specific MSigDB Gene Sets
- Source: R/data_generation.R
+ Source: R/data_generation.R
get_mgsigdb_gsets.Rd
@@ -88,26 +88,26 @@ Retrieve Organism-specific MSigDB Gene Sets
Arguments
- - species
+
+
+- species
species name, such as Homo sapiens, Mus musculus, etc.
See msigdbr_show_species for all the species available in
the msigdbr package
- collection
+- collection
collection. i.e., H, C1, C2, C3, C4, C5, C6, C7.
- subcollection
+- subcollection
sub-collection, such as CGP, BP, etc. (default = NULL,
i.e. list all gene sets in collection)
Value
-
-
-Retrieves the MSigDB gene sets and returns a list containing 2 elements:
gene_sets - A list containing the genes involved in each of the selected MSigDB gene sets
Retrieves the MSigDB gene sets and returns a list containing 2 elements:
gene_sets - A list containing the genes involved in each of the selected MSigDB gene sets
descriptions - A named vector containing the descriptions for each selected MSigDB gene set
@@ -133,16 +133,16 @@ Details
-
+
-
+
-
+
Retrieve Organism-specific PIN data
- Source: R/data_generation.R
+ Source: R/data_generation.R
get_pin_file.Rd
@@ -88,42 +88,42 @@ Retrieve Organism-specific PIN data
Arguments
- - source
+
+
+- source
As of this version, this function is implemented to get data
from 'BioGRID' only. This argument (and this wrapper function) was implemented
for future utility
- org
+- org
organism name. BioGRID naming requires underscores for spaces so
'Homo sapiens' becomes 'Homo_sapiens', 'Mus musculus' becomes 'Mus_musculus'
etc. See https://wiki.thebiogrid.org/doku.php/statistics for a full
list of available organisms (default = 'Homo_sapiens')
- path2pin
+- path2pin
the path of the file to save the PIN data. By default, the
PIN data is saved in a temporary file
- ...
+- ...
additional arguments for get_biogrid_pin
Value
-
-
-the path of the file in which the PIN data was saved. If
+
the path of the file in which the PIN data was saved. If
path2pin was not supplied by the user, the PIN data is saved in a
temporary file
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
pin_path <- get_pin_file()
-}
+} # }
@@ -138,16 +138,16 @@ Examples
-
+
-
+
-
+
Retrieve Reactome Pathway Gene Sets
- Source: R/data_generation.R
+ Source: R/data_generation.R
get_reactome_gsets.Rd
@@ -88,9 +88,7 @@ Retrieve Reactome Pathway Gene Sets
Value
-
-
-Gets the latest Reactome pathways gene sets in gmt format. Parses the
+
Gets the latest Reactome pathways gene sets in gmt format. Parses the
gmt file and returns a list containing 2 elements:
gene_sets - A list containing the genes involved in each Reactome pathway
descriptions - A named vector containing the descriptions for each Reactome pathway
@@ -107,16 +105,16 @@ Value
-
+
-
+
-
+
Retrieve Gene Sets from GMT-format File
- Source: R/data_generation.R
+ Source: R/data_generation.R
gset_list_from_gmt.Rd
@@ -88,19 +88,19 @@ Retrieve Gene Sets from GMT-format File
Arguments
-
Value
-
-
-list containing 2 elements:
gene_sets - A list containing the genes involved in each gene set
list containing 2 elements:
gene_sets - A list containing the genes involved in each gene set
descriptions - A named vector containing the descriptions for each gene set
@@ -116,16 +116,16 @@ Value
-
+
-
+
-
+
Hierarchical Clustering of Enriched Terms
- Source: R/clustering.R
+ Source: R/clustering.R
hierarchical_term_clustering.Rd
@@ -96,11 +96,13 @@ Hierarchical Clustering of Enriched Terms
Arguments
- - kappa_mat
+
+
+- kappa_mat
matrix of kappa statistics (output of create_kappa_matrix)
- enrichment_res
+- enrichment_res
data frame of pathfindR enrichment results. Must-have
columns are 'Term_Description' (if use_description = TRUE) or 'ID'
(if use_description = FALSE), 'Down_regulated', and 'Up_regulated'.
@@ -108,37 +110,35 @@
Arguments
provided.- num_clusters
+- num_clusters
number of clusters to be formed (default = NULL).
If NULL, the optimal number of clusters is determined as the number
which yields the highest average silhouette width.
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- clu_method
+- clu_method
the agglomeration method to be used
(default = 'average', see hclust)
- plot_hmap
+- plot_hmap
boolean to indicate whether to plot the kappa statistics
clustering heatmap or not (default = FALSE)
- plot_dend
+- plot_dend
boolean to indicate whether to plot the clustering
dendrogram partitioned into the optimal number of clusters (default = TRUE)
Value
-
-
-a vector of clusters for each enriched term in the enrichment results.
+ a vector of clusters for each enriched term in the enrichment results.
Details
@@ -153,10 +153,10 @@ Details
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
hierarchical_term_clustering(kappa_mat, enrichment_res)
hierarchical_term_clustering(kappa_mat, enrichment_res, method = 'complete')
-}
+} # }
@@ -171,16 +171,16 @@ Examples
-
+
-
+
-
+
Hypergeometric Distribution-based Hypothesis Testing
- Source: R/enrichment.R
+ Source: R/enrichment.R
hyperg_test.Rd
@@ -88,24 +88,24 @@ Hypergeometric Distribution-based Hypothesis Testing
Arguments
-
Value
-
-
-the p-value as determined using the hypergeometric distribution.
+ the p-value as determined using the hypergeometric distribution.
Details
@@ -137,16 +137,16 @@ Examples
-
+
Package index • pathfindR
-
+
-
+
-
+
@@ -93,7 +93,9 @@ Process Input
Arguments
- - input
+
+
+- input
the input data that pathfindR uses. The input must be a data
frame with three columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (OPTIONAL)
Arguments
- p_val_threshold
+- p_val_threshold
the p value threshold to use when filtering
the input data frame. Must a numeric value between 0 and 1. (default = 0.05)
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- convert2alias
+- convert2alias
boolean to indicate whether or not to convert gene symbols
in the input that are not found in the PIN to an alias symbol found in the PIN
(default = TRUE) IMPORTANT NOTE: the conversion uses human gene symbols/alias symbols.
Arguments
Value
-
-
-This function first filters the input so that all p values are less
+
This function first filters the input so that all p values are less
than or equal to the threshold. Next, gene symbols that are not found in
the PIN are identified. If aliases of these gene symbols are found in the
PIN, the symbols are converted to the corresponding aliases. The
@@ -173,16 +173,16 @@
Examples
-
+
-
+
-
+
@@ -88,7 +88,9 @@ Input Testing
Arguments
- - input
+
+
+- input
the input data that pathfindR uses. The input must be a data
frame with three columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (OPTIONAL)
Arguments
- p_val_threshold
+- p_val_threshold
the p value threshold to use when filtering
the input data frame. Must a numeric value between 0 and 1. (default = 0.05)
Value
-
-
-Only checks if the input and the threshold follows the required
+
Only checks if the input and the threshold follows the required
specifications.
@@ -133,16 +133,16 @@ Examples
-
+
-
+
-
+
@@ -88,15 +88,15 @@ Check if value is a valid color
Value
-
-
-TRUE if x is a valid color, otherwise FALSE
+ TRUE if x is a valid color, otherwise FALSE
@@ -111,16 +111,16 @@ Value
-
+
-
+
-
+
pathfindR: A package for Enrichment Analysis Utilizing Active Subnetworks
- Source: R/pathfindr.R
+ Source: R/pathfindr.R
pathfindr.Rd
@@ -138,16 +138,16 @@ Author
-
+
-
+
-
+
Plot the Heatmap of Score Matrix of Enriched Terms per Sample
- Source: R/scoring.R
+ Source: R/scoring.R
plot_scores.Rd
@@ -97,46 +97,46 @@ Plot the Heatmap of Score Matrix of Enriched Terms per Sample
Arguments
- - score_matrix
+
+
+- score_matrix
Matrix of agglomerated enriched term scores per sample. Columns are
samples, rows are enriched terms
- cases
+- cases
(Optional) A vector of sample names that are cases in the
case/control experiment. (default = NULL)
- label_samples
+- label_samples
Boolean value to indicate whether or not to label the
samples in the heatmap plot (default = TRUE)
- case_title
+- case_title
Naming of the 'Case' group (as in cases) (default = 'Case')
- control_title
+- control_title
Naming of the 'Control' group (default = 'Control')
- low
+- low
a string indicating the color of 'low' values in the coloring gradient (default = 'green')
- mid
+- mid
a string indicating the color of 'mid' values in the coloring gradient (default = 'black')
- high
+- high
a string indicating the color of 'high' values in the coloring gradient (default = 'red')
Value
-
-
-A `ggplot2` object containing the heatmap plot. x-axis indicates
+
A `ggplot2` object containing the heatmap plot. x-axis indicates
the samples. y-axis indicates the enriched terms. 'Score' indicates the
score of the term in a given sample. If cases are provided, the plot is
divided into 2 facets, named by case_title and control_title.
@@ -164,16 +164,16 @@ Examples
-
+
-
+
-
+
Process Data frame of Protein-protein Interactions
- Source: R/data_generation.R
+ Source: R/data_generation.R
process_pin.Rd
@@ -88,16 +88,16 @@ Process Data frame of Protein-protein Interactions
Arguments
-
Value
-
-
-processed PIN data frame (removes self-interactions and
+
processed PIN data frame (removes self-interactions and
duplicated interactions)
@@ -113,16 +113,16 @@ Value
-
+
-
+
-
+
Return The Path to Given Protein-Protein Interaction Network (PIN)
- Source: R/utility.R
+ Source: R/utility.R
return_pin_path.Rd
@@ -100,7 +100,9 @@ Return The Path to Given Protein-Protein Interaction Network (PIN)
Arguments
- - pin_name_path
+
+
+
Value
-
-
-The absolute path to chosen PIN.
+ The absolute path to chosen PIN.
See also
@@ -120,9 +120,9 @@ See also
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
pin_path <- return_pin_path('GeneMania')
-}
+} # }
@@ -137,16 +137,16 @@ Examples
-
+
-
+
-
+
Wrapper Function for pathfindR - Active-Subnetwork-Oriented Enrichment Workflow
- Source: R/core.R
+ Source: R/core.R
run_pathfindr.Rd
@@ -103,7 +103,9 @@ Wrapper Function for pathfindR - Active-Subnetwork-Oriented Enrichment Workf
Arguments
- - input
+
+
+- input
the input data that pathfindR uses. The input must be a data
frame with three columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (OPTIONAL)
Arguments
- gene_sets
+- gene_sets
Name of the gene sets to be used for enrichment analysis.
Available gene sets are 'KEGG', 'Reactome', 'BioCarta', 'GO-All',
'GO-BP', 'GO-CC', 'GO-MF', 'cell_markers', 'mmu_KEGG' or 'Custom'.
@@ -119,73 +121,71 @@
Arguments
must be specified. (Default = 'KEGG')- min_gset_size
+- min_gset_size
minimum number of genes a term must contain (default = 10)
- max_gset_size
+- max_gset_size
maximum number of genes a term must contain (default = 300)
- custom_genes
+- custom_genes
a list containing the genes involved in each custom
term. Each element is a vector of gene symbols located in the given custom
term. Names should correspond to the IDs of the custom terms.
- custom_descriptions
+- custom_descriptions
A vector containing the descriptions for each
custom term. Names of the vector should correspond to the IDs of the custom
terms.
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- p_val_threshold
+- p_val_threshold
the p value threshold to use when filtering
the input data frame. Must a numeric value between 0 and 1. (default = 0.05)
- enrichment_threshold
+- enrichment_threshold
adjusted-p value threshold used when filtering
enrichment results (default = 0.05)
- convert2alias
+- convert2alias
boolean to indicate whether or not to convert gene symbols
in the input that are not found in the PIN to an alias symbol found in the PIN
(default = TRUE) IMPORTANT NOTE: the conversion uses human gene symbols/alias symbols.
- plot_enrichment_chart
+- plot_enrichment_chart
boolean value. If TRUE, a bubble chart displaying
the enrichment results is plotted. (default = TRUE)
- output_dir
+- output_dir
the directory to be created where the output and intermediate
files are saved (default = NULL, a temporary directory is used)
- list_active_snw_genes
+- list_active_snw_genes
boolean value indicating whether or not to report
the non-significant active subnetwork genes for the active subnetwork which was enriched for
the given term with the lowest p value (default = FALSE)
- ...
+- ...
additional arguments for active_snw_enrichment_wrapper
Value
-
-
-Data frame of pathfindR enrichment results. Columns are:
- ID
+ Data frame of pathfindR enrichment results. Columns are:
- ID
ID of the enriched term
- Term_Description
@@ -220,8 +220,6 @@ Value
results linked to the visualizations of the enriched terms in addition to
the table of converted gene symbols. This report can be found in
'output_dir/results.html' under the current working directory.
-
-
By default, a bubble chart of top 10 enrichment results are plotted. The x-axis
corresponds to fold enrichment values while the y-axis indicates the enriched
terms. Sizes of the bubbles indicate the number of significant genes in the given terms.
@@ -263,9 +261,9 @@
See also
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
run_pathfindR(example_pathfindR_input)
-}
+} # }
@@ -280,16 +278,16 @@ Examples
-
+
-
+
-
+
Calculate Agglomerated Scores of Enriched Terms for Each Subject
- Source: R/scoring.R
+ Source: R/scoring.R
score_terms.Rd
@@ -95,7 +95,9 @@ Calculate Agglomerated Scores of Enriched Terms for Each Subject
Arguments
- - enrichment_table
+
+
+- enrichment_table
a data frame that must contain the 3 columns below:
- Term_Description
Description of the enriched term (necessary if use_description = TRUE)
Arguments
- exp_mat
+- exp_mat
the experiment (e.g., gene expression/methylation) matrix.
Columns are samples and rows are genes. Column names must contain sample
names and row names must contain the gene symbols.
- cases
+- cases
(Optional) A vector of sample names that are cases in the
case/control experiment. (default = NULL)
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- plot_hmap
+- plot_hmap
Boolean value to indicate whether or not to draw the
heatmap plot of the scores. (default = TRUE)
- ...
+- ...
Additional arguments for plot_scores for aesthetics
of the heatmap plot
Value
-
-
-Matrix of agglomerated scores of each enriched term per sample.
+
Matrix of agglomerated scores of each enriched term per sample.
Columns are samples, rows are enriched terms. Optionally, displays a heatmap
of this matrix.
Conceptual Background
-
+
For an experiment matrix (containing expression, methylation, etc. values),
the rows of which are genes and the columns of which are samples,
@@ -193,16 +193,16 @@
Examples
-
+
-
+
-
+
Active Subnetwork Search + Enrichment Analysis Wrapper for a Single Iteration
- Source: R/utility.R
+ Source: R/utility.R
single_iter_wrapper.Rd
@@ -115,119 +115,121 @@ Active Subnetwork Search + Enrichment Analysis Wrapper for a Single Iteratio
Arguments
- - i
+
+
+- i
current iteration index (default = NULL)
- dirs
+- dirs
vector of directories for parallel runs
- input_processed
+- input_processed
processed input data frame
- pin_path
+- pin_path
path/to/PIN/file
- score_quan_thr
+- score_quan_thr
active subnetwork score quantile threshold. Must be
between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
- sig_gene_thr
+- sig_gene_thr
threshold for the minimum proportion of significant genes in
the subnetwork (Default = 0.02) If the number of genes to use as threshold is
calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number
is set to 2
- search_method
+- search_method
algorithm to use when performing active subnetwork
search. Options are greedy search (GR), simulated annealing (SA) or genetic
algorithm (GA) for the search (default = 'GR').
- silent_option
+- silent_option
boolean value indicating whether to print the messages
to the console (FALSE) or not (TRUE, this will print to a temp. file) during
active subnetwork search (default = TRUE). This option was added because
during parallel runs, the console messages get disorderly printed.
- use_all_positives
+- use_all_positives
if TRUE: in GA, adds an individual with all positive
nodes. In SA, initializes candidate solution with all positive nodes. (default = FALSE)
- geneInitProbs
+- geneInitProbs
For SA and GA, probability of adding a gene in initial solution (default = 0.1)
- saTemp0
+- saTemp0
Initial temperature for SA (default = 1.0)
- saTemp1
+- saTemp1
Final temperature for SA (default = 0.01)
- saIter
+- saIter
Iteration number for SA (default = 10000)
- gaPop
+- gaPop
Population size for GA (default = 400)
- gaIter
+- gaIter
Iteration number for GA (default = 200)
- gaThread
+- gaThread
Number of threads to be used in GA (default = 5)
- gaCrossover
+- gaCrossover
Applies crossover with the given probability in GA (default = 1, i.e. always perform crossover)
- gaMut
+- gaMut
For GA, applies mutation with given mutation rate (default = 0, i.e. mutation off)
- grMaxDepth
+- grMaxDepth
Sets max depth in greedy search, 0 for no limit (default = 1)
- grSearchDepth
+- grSearchDepth
Search depth in greedy search (default = 1)
- grOverlap
+- grOverlap
Overlap threshold for results of greedy search (default = 0.5)
- grSubNum
+- grSubNum
Number of subnetworks to be presented in the results (default = 1000)
- gset_list
+- gset_list
list for gene sets
- adj_method
+- adj_method
correction method to be used for adjusting p-values.
(default = 'bonferroni')
- enrichment_threshold
+- enrichment_threshold
adjusted-p value threshold used when filtering
enrichment results (default = 0.05)
- list_active_snw_genes
+- list_active_snw_genes
boolean value indicating whether or not to report
the non-significant active subnetwork genes for the active subnetwork which was enriched for
the given term with the lowest p value (default = FALSE)
Arguments
Value
-
-
-Data frame of enrichment results using active subnetwork search results
+ Data frame of enrichment results using active subnetwork search results
@@ -252,16 +252,16 @@ Value
-
+
-
+
-
+
Summarize Enrichment Results
- Source: R/enrichment.R
+ Source: R/enrichment.R
summarize_enrichment_results.Rd
@@ -88,7 +88,9 @@ Summarize Enrichment Results
Arguments
- - enrichment_res
+
+
+- enrichment_res
a dataframe of combined enrichment results. Columns are:
- ID
ID of the enriched term
Arguments
- list_active_snw_genes
+- list_active_snw_genes
boolean value indicating whether or not to report
the non-significant active subnetwork genes for the active subnetwork which was enriched for
the given term with the lowest p value (default = FALSE)
Arguments
Value
-
-
-a dataframe of summarized enrichment results (over multiple iterations). Columns are:
- ID
+ a dataframe of summarized enrichment results (over multiple iterations). Columns are:
- ID
ID of the enriched term
- Term_Description
@@ -150,9 +150,9 @@ Value
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
summarize_enrichment_results(enrichment_res)
-}
+} # }
@@ -167,16 +167,16 @@ Examples
-
+
-
+
-
+
@@ -95,7 +95,9 @@ Create Term-Gene Graph
Arguments
- - result_df
+
+
+- result_df
A dataframe of pathfindR results that must contain the following columns:
- Term_Description
Description of the enriched term (necessary if use_description = TRUE)
Arguments
- num_terms
+- num_terms
Number of top enriched terms to use while creating the graph. Set to NULL to use
all enriched terms (default = 10, i.e. top 10 terms)
- layout
+- layout
The type of layout to create (see ggraph for details. Default = 'stress')
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- node_size
+- node_size
Argument to indicate whether to use number of significant genes ('num_genes')
or the -log10(lowest p value) ('p_val') for adjusting the node sizes (default = 'num_genes')
- node_colors
+- node_colors
vector of 3 colors to be used for coloring nodes (colors for term nodes, up, and down, respectively)
Value
-
-
-a ggraph object containing the term-gene graph.
+
a ggraph object containing the term-gene graph.
Each node corresponds to an enriched term (beige), an up-regulated gene (green)
or a down-regulated gene (red). An edge between a term and a gene indicates
that the given term involves the gene. Size of a term node is proportional
@@ -179,16 +179,16 @@
Examples
-
+
-
+
-
+
Create Terms by Genes Heatmap
- Source: R/visualization.R
+ Source: R/visualization.R
term_gene_heatmap.Rd
@@ -99,7 +99,9 @@ Create Terms by Genes Heatmap
Arguments
- - result_df
+
+
+- result_df
A dataframe of pathfindR results that must contain the following columns:
- Term_Description
Description of the enriched term (necessary if use_description = TRUE)
Arguments
- genes_df
+- genes_df
the input data that was used with run_pathfindR.
It must be a data frame with 3 columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (optional)
Arguments
The change values in this data frame are used to color the affected genes
- num_terms
+- num_terms
Number of top enriched terms to use while creating the plot. Set to NULL to use
all enriched terms (default = 10)
- use_description
+- use_description
Boolean argument to indicate whether term descriptions
(in the 'Term_Description' column) should be used. (default = FALSE)
- low
+- low
a string indicating the color of 'low' values in the coloring gradient (default = 'green')
- mid
+- mid
a string indicating the color of 'mid' values in the coloring gradient (default = 'black')
- high
+- high
a string indicating the color of 'high' values in the coloring gradient (default = 'red')
- legend_title
+- legend_title
legend title (default = 'change')
- sort_terms_by_p
+- sort_terms_by_p
boolean to indicate whether to sort terms by 'lowest_p'
(TRUE) or by number of genes (FALSE) (default = FALSE)
- ...
+- ...
additional arguments for input_processing (used if
genes_df is provided)
Value
-
-
-a ggplot2 object of a heatmap where rows are enriched terms and
+
a ggplot2 object of a heatmap where rows are enriched terms and
columns are involved input genes. If genes_df is provided, colors of
the tiles indicate the change values.
@@ -190,16 +190,16 @@ Examples
-
+
-
+
-
+
Visualize Human KEGG Pathways
- Source: R/visualization.R
+ Source: R/visualization.R
visualize_KEGG_diagram.Rd
@@ -94,19 +94,21 @@ Visualize Human KEGG Pathways
Arguments
- - kegg_pw_ids
+
+
+- kegg_pw_ids
KEGG ids of pathways to be colored and visualized
- input_processed
+- input_processed
input data processed via input_processing
- scale_vals
+- scale_vals
should change values be scaled? (default = TRUE)
- node_cols
+- node_cols
low, middle and high color values for coloring the pathway nodes
(default = NULL). If node_cols=NULL, the low, middle and high color
are set as 'green', 'gray' and 'red'. If all change values are 1e6 (in case no
@@ -114,16 +116,14 @@
Arguments
input_processing), only one color ('#F38F18' if NULL) is used.- legend.position
+- legend.position
the default position of legends ("none", "left",
"right", "bottom", "top", "inside")
Value
-
-
-Creates colored visualizations of the enriched human KEGG pathways
+
Creates colored visualizations of the enriched human KEGG pathways
and returns them as a list of ggplot objects, named by Term ID.
@@ -135,13 +135,13 @@ See also
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
input_processed <- data.frame(
GENE = c("PKLR", "GPI", "CREB1", "INS"),
CHANGE = c(1.5, -2, 3, 5)
)
gg_list <- visualize_KEGG_diagram(c("hsa00010", "hsa04911"), input_processed)
-}
+} # }
@@ -156,16 +156,16 @@ Examples
-
+
-
+
-
+
Visualize Active Subnetworks
- Source: R/active_snw_search.R
+ Source: R/active_snw_search.R
visualize_active_subnetworks.Rd
@@ -97,11 +97,13 @@ Visualize Active Subnetworks
Arguments
- - active_snw_path
+
+
+- active_snw_path
path to the output of an Active Subnetwork Search
- genes_df
+- genes_df
the input data that was used with run_pathfindR.
It must be a data frame with 3 columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (optional)
Arguments
The change values in this data frame are used to color the affected genes
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- num_snws
+- num_snws
number of top subnetworks to be visualized (leave blank if
you want to visualize all subnetworks)
- layout
+- layout
The type of layout to create (see ggraph for details. Default = 'stress')
- score_quan_thr
+- score_quan_thr
active subnetwork score quantile threshold. Must be
between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
- sig_gene_thr
+- sig_gene_thr
threshold for the minimum proportion of significant genes in
the subnetwork (Default = 0.02) If the number of genes to use as threshold is
calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number
is set to 2
- ...
+- ...
additional arguments for input_processing
Value
-
-
-a list of ggplot objects of graph visualizations of identified active
+
a list of ggplot objects of graph visualizations of identified active
subnetworks. Green nodes are down-regulated genes, reds are up-regulated genes
and yellows are non-input genes
@@ -182,16 +182,16 @@ Examples
-
+
-
+
-
+
Visualize Interactions of Genes Involved in the Given Enriched Terms
- Source: R/visualization.R
+ Source: R/visualization.R
visualize_term_interactions.Rd
@@ -88,27 +88,27 @@ Visualize Interactions of Genes Involved in the Given Enriched Terms
Arguments
- - result_df
+
+
+- result_df
Data frame of enrichment results. Must-have columns
are: 'Term_Description', 'Up_regulated' and 'Down_regulated'
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- show_legend
+- show_legend
Boolean to indicate whether to display the legend (TRUE)
or not (FALSE) (default: TRUE)
Value
-
-
-list of ggplot objects (named by Term ID) visualizing the interactions of genes involved
+
list of ggplot objects (named by Term ID) visualizing the interactions of genes involved
in the given enriched terms (annotated in the result_df) in the PIN used
for enrichment analysis (specified by pin_name_path).
@@ -131,10 +131,10 @@ See also
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
result_df <- example_pathfindR_output[1:2, ]
gg_list <- visualize_term_interactions(result_df, pin_name_path = 'IntAct')
-}
+} # }
@@ -149,16 +149,16 @@ Examples
-
+
-
+
-
+
Create Diagrams for Enriched Terms
- Source: R/visualization.R
+ Source: R/visualization.R
visualize_terms.Rd
@@ -94,30 +94,32 @@ Create Diagrams for Enriched Terms
Arguments
- - result_df
+
+
+- result_df
Data frame of enrichment results. Must-have columns for
KEGG human pathway diagrams (is_KEGG_result = TRUE) are: 'ID' and 'Term_Description'.
Must-have columns for the rest are: 'Term_Description', 'Up_regulated' and
'Down_regulated'
- input_processed
+- input_processed
input data processed via input_processing,
not necessary when is_KEGG_result = FALSE
- is_KEGG_result
+- is_KEGG_result
boolean to indicate whether KEGG gene sets were used for
enrichment analysis or not (default = TRUE)
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- ...
+- ...
additional arguments for visualize_KEGG_diagram (used
when is_KEGG_result = TRUE) or visualize_term_interactions
(used when is_KEGG_result = FALSE)
Arguments
Value
-
-
-Depending on the argument is_KEGG_result, creates visualization of
- interactions of genes involved in the list of enriched terms in
-
-result_df. Returns a list of ggplot objects named by Term ID.
+ Depending on the argument is_KEGG_result, creates visualization of
+ interactions of genes involved in the list of enriched terms in
+ result_df. Returns a list of ggplot objects named by Term ID.
Details
@@ -150,7 +149,7 @@ See also
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
input_processed <- data.frame(
GENE = c("PARP1", "NDUFA1", "STX6", "SNAP23"),
CHANGE = c(1.5, -2, 3, 5)
@@ -159,7 +158,7 @@ Examples
gg_list <- visualize_terms(result_df, input_processed)
gg_list2 <- visualize_terms(result_df, is_KEGG_result = FALSE, pin_name_path = 'IntAct')
-}
+} # }
@@ -174,16 +173,16 @@ Examples
-
+
Visualization of pathfindR Enrichment Results
-2024-05-04
+2024-10-14
- Source:vignettes/visualization_vignette.Rmd
+ Source: vignettes/visualization_vignette.Rmd
visualization_vignette.Rmd
By default the node sizes are plotted proportional to the number of
genes a term contains (num_genes). To adjust node sizes
-using the \(-log_{10}\)(lowest p
-values), set node_size = "p_val":
node_size = "p_val":
term_gene_graph(example_pathfindR_output, num_terms = 3, node_size = "p_val")See ?term_gene_graph for more details.
+
Authors and Citation
- Version dev -
- Version 2.4 -
- Version 2.3 -
- Version 2.2 -
- Version 2.1 -
- Version 2.0 -
- Version 1.6 -
- Version 1.5 -
- Version 1.4 -
- Version 1.3 -
- Version 1.2 -
- Version 1.1 -
pathfindR 2.4.12024-05-04
+Minor Changes and Bug Fixes
+- fixed a bug regarding KEGG gene set fetching: removed the conversion functionality in
get_kegg_gsets()which now returns KEGG IDs so that the user can convert the returned identifiers using a more appropriate tool (e.g. BioMart) should they wish
+
pathfindR 2.4.02024-04-28
+Major Changes
+- implemented a new
color_kegg_pathway()function usingggkeggto create colored KEGG pathway ggplot objects (instead of usingKEGGRESTto obtain the colored PNG files, which no longer works #169)
+ - renamed the
visualize_hsa_KEGGfunction tovisualize_KEGG_diagram()to reflect this is now able to handle KEGG pathway enrichment results from any organism
+ - updated the
visualize_terms(),visualize_term_interactions()andvisualize_KEGG_diagram()functions so that they now return a list of ggplot objects (named by term ID)
+ - updated the
get_kegg_gsets()function to also useggkeggfor fetching genes per pathway data
+ - removed unneeded dependencies:
magick,KEGGgraphandKEGGREST+
+
Minor Changes and Bug Fixes
+- updated the
get_biogrid_pin()function so that it can now determine the latest version and download/process it from BioGRID (via settingrelease = "latest", which is now the default behavior)
+
pathfindR 2.3.12024-01-19
+Minor Changes and Bug Fixes
+- fixed a bug in the
UpSet_plot()plot function regarding the interaction withggupsetpackage that was discovered in a reverse dependency check forggplot2 3.5.0(#189)
+ - fixed gene symbol case mismatch issue in
score_terms()(#186)
+ - applied enhancement suggestion from #184 to enable scale fill manual for
term_gene_graph()+
+
pathfindR 2.3.02023-10-08
+Major Changes
+- reverted removal of
create_HTML_report()sorun_pathfindR()once again generates HTML reports
+
pathfindR 2.2.02023-08-20
+Minor Changes and Bug Fixes
+- added the
disable_parallelargument inactive_snw_enrichment_wrapper()to be able to disable parallel runs viaforeach+
+ - fixed the issue encountered on CentOS where
forechwasn’t loadingpathfindR(#164)
+ - fixed a CRAN error due to a package documentation issue (#172) +
- performed some refactoring and updated/improved all tests +
pathfindR 2.1.02023-05-12
+Minor Changes and Bug Fixes
+- removed
create_HTML_report()sorun_pathfindR()no longer generates a HTML report
+
pathfindR 2.0.12023-05-10
+Minor Changes and Bug Fixes
+- added the
dir_for_reportargument in the internal functioncreate_HTML_report()to fix test issues on CRAN
+
pathfindR 2.0.02023-05-06
+Major Changes
+- updated the java active subnetwork search component and added the
seedForRandomargument inactive_snw_search()to ensure reproducibility. By default behavior, inrun_pathfindR(), a seed is set for each iteration to produce reproducible results (#108)
+ - as the example input/output data were renamed for convenience in ‘pathfindR.data’ v2.0, ‘pathfindR’ now depends on pathfindR.data (>= 2.0) +
- refactored/simplified
run_pathfindR()+
+ - visualization enriched term diagrams are now NOT part of
run_pathfindR()+
+ - default behavior of
run_pathfindR()is now to run in a temporary directory. The user can still setoutput_dirto run in a specified directory and also produce HTML reports
+ - in
hierarchical_term_clustering(), update the sequence of number of clusters for which silhouette width is calculated for choosing the optimal number of clusters. This should speed up the function for cases with a large number of enriched terms
+ - updated the relevant vignettes to reflect the implemented changes +
Minor Changes and Bug Fixes
+- fixed a minor issue in
return_pin_path()where the PIN was not properly read (#157)
+
pathfindR 1.6.42022-08-25
+Minor Changes and Bug Fixes
+- updated the alias selection function within
input_processing()so that an alias that is not already present is selected
+ - updated the min-max scaling (controlled by
scale_vals) incolor_kegg_pathway(), the default is nowscale_vals=TRUE+
+ - updated the
term_gene_heatmap()function so that legend title is shown and can be customized
+ - updated the
term_gene_heatmap()function so that coloring is proper when no change values are provided ingenes_df+
+ - added the
sort_terms_by_pargument to theterm_gene_heatmap()function to enable sorting of terms by ‘lowest_p’
+ - in visualization functions, made coloring of up-/down-regulated genes consistent (#126) +
- added the
vertex.label.cexandvertex.size.scalingarguments tocluster_graph_vis()+
+ - added the
show_legendargument tovisualize_term_interactions()to toggle the legend
+
pathfindR 1.6.32021-11-15
+Minor Changes and Bug Fixes
+- Fixed coloring issue in
color_kegg_pathway()+
+ - In
color_kegg_pathway()the default value fornormalize_valsis nowFALSE+
+
pathfindR 1.6.22021-08-21
+Major Changes
+- fixed an issue in
get_kegg_gsets()where empty result was returned for some organisms due to an error in parsing (#72)
+
Minor Changes and Bug Fixes
+- added
repel = TRUEinterm_gene_graph()andcombined_results_graph()for better visualization of labels
+ - fixed minor issue in
enrichment_chart()(#75)
+ - fixed minor issue in
visualize_term_interactions()+
+ - fixed issue in
get_biogrid_pin()where the download method was set towget(now set toauto, per #83)
+ - updated to using tab3 format for
get_biogrid_pin()(if tab3 is available for the chosen release, otherwise tab2 format is used)
+ - updated the default version of PIN obtained by
get_biogrid_pin()to ‘4.4.200’
+ - in
get_kegg_gsets(), improved parsing of KEGG term descriptions so that no description is duplicated (#87)
+ - in
score_terms(), if using descriptions, the ID is now appended for (any) duplicated term descriptions (#87)
+ - in
obtain_colored_url(), swappedbg_colorwithfg_colordue to an issue withKEGGREST+
+ - added legend to
term_gene_heatmap()(#95)
+ - in
get_biogrid_pin(), the “download.file.method” from global options is used
+ -
+
combined_results_graph()raises an error if there are no common terms in the combined data frame
+
pathfindR 1.6.12021-01-04
+Major Changes
+- In
run_pathfindR(), the defaultiterationswas set back to 10 (the default for all other v1.x)
+
pathfindR 1.6.02020-11-21
+Major Changes
+- In
run_pathfindR(), as “GR” (the default active subnetwork search method) provides nearly identical results in each iteration, the defaultiterationsis set to 1
+ - added the column ‘support’ (the proportion of active subnetworks leading to enrichment over all subnetworks) in the output +
- updated the download URL in
get_biogrid_pin()as BioGRID updated the URL for download
+
Minor Changes and Bug Fixes
+- changed old argument in the “Step-by-Step Execution of the pathfindR Enrichment Workflow” vignette +
- fixed an issue in
visualize_term_interactions()where the file name was too long, it was causing an error on Windows. Limited to 100 characters (#58)
+
pathfindR 1.5.12020-09-20
+Minor Changes and Bug Fixes
+- Fixed issue in
check_java_version()where java version 14 could not be parsed (#49)
+ - Fixed issue in
combined_results_graph()where gene nodes were not colored correctly (#55)
+
pathfindR 1.5.02020-06-06
+Major Changes
+- created separate package
pathfindR.datafor storing pathfindR data
+ - added the function
visualize_active_subnetworks()for visualizing graphs of active subnetworks
+ - add the new vignette “Comparing Two pathfindR Results” that briefly describes how different pathfindR results can be compared +
- added the functions
combine_pathfindR_results()andcombined_results_graph()for comparison of 2 pathfindR results and term-gene graph of the combined results, respectively
+ - added the function
get_pin_file()for obtaining organism-specific PIN data (only from BioGRID for now)
+ - added the function
get_gene_sets_list()for obtaining organism-specific gene sets list from KEGG, Reactome and MSigDB
+ - added the function
term_gene_heatmap()to create heatmap visualizations of enriched terms and the involved input genes. Rows are enriched terms and columns are involved input genes. Ifgenes_dfis provided, colors of the tiles indicate the change values
+ - added the function
UpSet_plot()to create UpSet plots of enriched terms
+ - added the human cell markers gene sets data
cell_markers_gsetsandcell_markers_descriptions+
+
Minor Changes and Bug Fixes
+- fixed an issue regarding
parallel::makeCluster()inrun_pathfindR()(#45)
+ - fixed save-related issue in
download_kegg_png()(#37, @rix133)
+ - added the output data
RA_comparison_outputof pathfindR results on another RA-related dataset (GSE84074)
+ - in
visualize_hsa_KEGG(), fixed the issue where >1 entrez ids were returned for a gene symbol (the first one is kept)
+ - in
visualize_hsa_KEGG(), implemented a tryCatch to avoid any issues whenKEGGREST::color.pathway.by.objects()might fail (#28)
+ - in
visualize_hsa_KEGG(), now limiting the number of genes passes ontoKEGGREST::color.pathway.by.objects()to < 60 (because the KEGG API now limits the number?)
+ - changed default visualization in
term_gene_heatmap()(i.e. whengenes_dfis not provided) to binary colored heatmap (by default, “green” and “red”, controlled bylowandhigh) by up-/down- regulation status
+ - update the vignette “pathfindR Analysis for non-Homo-sapiens organisms” to reflect new data generation functions
get_pin_file()andget_gene_sets_list()and fixed a minor issue in the vignette (#46)
+
pathfindR 1.4.22019-12-06
+Minor Changes and Bug Fixes
+- Fixed corner case in
create_kappa_matrix()whenchanceis 1, the metric is turned into 0
+ - Fixed misused
class(.) == *incluster_graph_vis()+
+
pathfindR 1.4.12019-11-15
+Major Changes
+- Fixed error in DESCRIPTION: the Java version in SystemRequirements was corrected to “Java (>= 8.0)” +
- The Java version is now checked +
Minor Changes and Bug Fixes
+- Fixed behavior: when no input genes are present in the enriched hsa KEGG pathway, visualization of the pathway is now skipped +
- Added the argument
max_to_plottovisualize_hsa_KEGG()and torun_pathfindR(). This argument controls the number of pathways to be visualized (default is NULL, i.e. no filter). This was implemented not to slow down the runtime ofrun_pathfindR()as downloading the png files is slow.
+ - Fixed links to visualizations in
enriched_ters.Rmd+
+
pathfindR 1.4.02019-11-08
+Major Changes
+- Replaced most occurrences of “pathway” to “term”. This was adapted because “term” reflects the utility of the package better. The enrichment and clustering approaches work with any kind of gene set data (be it pathway gene sets, gene ontology gene sets, motif gene sets etc.) Accordingly:
+
-
+
DESCRIPTIONwas updated
+ - The functions
annotate_pathway_DEGs(),calculate_pw_scores(),cluster_pathways(),fuzzy_pw_clustering(),hierarchical_pw_clustering(),visualize_pw_interactions()andvisualize_pws()were renamed toannotate_term_DEGs(),score_terms(),cluster_enriched_terms(),fuzzy_term_clustering(),hierarchical_term_clustering(),visualize_term_interactions()andvisualize_terms()respectively
+ - The Rmd template file for the report
enriched_pathways.Rmdwas renamed toenriched_terms.Rmd+
+ - All the Rmd template files for the report were updated +
- Documentation of each function was updated accordingly +
+ -
+
- Added the visualization function
term_gene_graph(), which creates a graph of enriched terms - involved genes
+ - Made changes in
enrichment()andenrichment_analyses()to get enrichment results faster
+ - Added the function
fetch_gene_set()for obtaining gene set data more easily
+ - Terms in gene sets can now be filtered according to the number of genes a term contains (controlled by
min_gset_size,max_gset_sizeinfetch_gene_set()andrun_pathfindR())
+ - Added the argument
gaCrossoverduring active subnetwork search which controls the probability of a crossover in GA (default = 1, i.e. always perform crossover)
+ - Added unit tests using
testthat+
+ - Updated all gene sets data +
- Updated all RA example data +
- The vignettes were updated +
- Updated all PIN data +
- Improved speed of kappa matrix calculation (
create_kappa_matrix())
+ - Added vignette for non-Homo-sapiens organisms +
- Added Mus musculus (mmu) data:
+
-
+
mmu_kegg_genes&mmu_kegg_descriptions: mmu KEGG gene sets data
+ - mmu STRING PIN +
-
+
myeloma_input&myeloma_output: example mmu input and output data
+
+ -
+
- Added the STRING PIN (combined score >= 400) +
- The argument
sig_gene_thrin subnetwork filtering viafilterActiveSnws()now serves the threshold proportion of significant genes in the active subnetwork. e.g., if there are 100 significant genes andsig_gene_thr = 0.03, subnetwork that contain at least 3 (100 x 0.03) significant genes will be accepted for further analysis
+ - Removed
pathviewdependency by implementing colored pathway diagram visualization function usingKEGGRESTandKEGGgraph+
+
Minor Changes and Bug Fixes
+- In
hierarchical_term_clustering(), redefined the distance measure as1 - kappa statistic+
+ - Fixed minor issue in
cluster_graph_vis()(during the calculations for additional node colors)
+ - Removed title from graph visualization of hierarchical clustering in
cluster_graph_vis()+
+ - In
active_snw_search(), unnecessary warnings during active subnetwork search were removed
+ - Fixed minor issue in
enrichment_chart(), supplying fuzzy clustered results no longer raises an error
+ - Added new checks in
input_testing()andinput_processing()to ensure that both the initial input data frame and the processed input data frame for active subnetwork search contain at least 2 genes (to fix the corner case encountered in issue #17)
+ - Fixed minor issue in
enrichment_chart(), ensuring that bubble sizes displayed in the legend (proportional to # of DEGs) are integers
+ - In
enrichment_chart(), added the argumentsnum_bubbles(default is 4) to control number of bubbles displayed in the legend andeven_breaks(default isTRUE) to indicate if even increments of breaks are required
+ - Updated the logo +
- Minor fix in
term_gene_graph()(create the igraph object as an undirected graph for better auto layout)
+ - Minor fix in
visualize_term_interactions(). The legend no longer displays “Non-input Active Snw. Genes” if they were not provided
+ - The argument
human_genesinrun_pathfindR()andinput_processing()was renamed asconvert2alias+
+ - The gene symbols in the input data frame, the PIN and the gene sets are now turned into uppercase (for obtaining the best overlap) +
- Added the argument
top_termstoenrichment_chart(), controlling the number top enriched terms to plot (default is 10)
+ - Other minor bug/error fixes +
pathfindR 1.3.02018-11-20
+Major Changes
+- Separated the steps of the function
run_pathfindRinto individual functions:active_snw_search,enrichment_analyses,summarize_enrichment_results,annotate_pathway_DEGs,visualize_pws.
+ - renamed the function
pathmapasvisualize_hsa_KEGG, updated the function to produce different visualizations for inputs with binary change values (ordered) and no change values (theinput_processingfunction, assigns a change value of 100 to all).
+ - Created new the visualization function
visualize_pw_interactions, which creates PNG files visualizing the interactions (in the selected PIN) of genes involved in the given pathways.
+ - Added new vignette, describing the step-by-step execution of the pathfindR workflow +
- Changed clustering metric to kappa statistic, created the new clustering related functions
create_kappa_matrix,hierarchical_pw_clustering,fuzzy_pw_clusteringandcluster_pathways.
+ - Implemented the new function
cluster_graph_visfor visualizing graph diagrams of clustering results.
+
Minor Changes and Bug Fixes
+- Fixed the bug where the arguments
score_quan_thrandsig_gene_thrforrun_pathfindRwere not being utilized.
+ - in
run_pathfindR, added message at the end of run, reporting the number enriched pathways.
+ - the function
run_pathfindRnow creates a variableorg_dirthat is the “path/to/original/working/directory”.org_diris used in multiple functions to return to the original working directory if anything fails. This changes the previous behavior where if a function stopped with an error the directory was changed to “..”, i.e. the parent directory. This change was adapted so that the user is returned to the original working directory if they supply a recursive output folder (output_dir, e.g. “./ALL_RESULTS/RESULT_A”).
+ - in
input_processing, added the argumenthuman_genesto only perform alias symbol conversion when human gene symbols are provided. - Updated the Rmd files used to create the report HTML files
+ - Added the data for
GO-All, all annotations in the GO database (BP+MF+CC)
+ - Updated the vignette
pathfindR - An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworksto reflect the new functionalities.
+
pathfindR 1.2.32018-10-26
+Minor Changes and Bug Fixes
+- in the function
plot_scores, added the argumentlabel_casesto indicate whether or not to label the cases in the pathway scoring heatmap plot. Also added the argumentcase_control_titleswhich allows the user to change the default “Case” and “Control” headers. Also added the argumentslowandhighused to change the low and high end colors of the scoring color gradient.
+ - in the function
plot_scores, reversed the color gradient to match the coloring scheme used by pathview (i.e. red for positive values, green for negative values)
+ - minor change in
parseActiveSnwSearch, replacedscore_thrbyscore_quan_thr. This was done so that the scoring filter for active subnetworks could be performed based on the distribution of the current active subnetworks and not using a constant empirical score value threshold.
+ - minor change in
parseActiveSnwSearch, increasedsig_gene_thrfrom 2 to 10 as we observed in most of the cases, this resulted in faster runs with comparable results.
+ - in
choose_clusters, added the argumentp_val_thresholdto be used as p value threshold for filtering the enriched pathways prior to clustering.
+
pathfindR 1.2.12018-08-17
+Major Changes
+- Added the option to specify a custom gene set when using
run_pathfindR. For this, thegene_setsargument should be set to “Custom” andcustom_genesandcustom_pathwaysshould be provided.
+
Minor Changes and Bug Fixes
+- fixed minor bug in
calculate_pw_scoreswhere if there was one DEG, subsetting the experiment matrix failed
+ - added if condition to check if there were DEGs in
calculate_pw_scores. If there is none, the pathway is skipped.
+ - in
calculate_pw_scores, ifcasesare provided, the pathways are reordered before plotting the heat map and returning the matrix according to their activity incases. This way, “up” pathways are grouped together, same for “down” pathways.
+ - in
calculate_pwd, if a pathway has perfect overlap with other pathways, change the correlation value with 1 instead of NA.
+ - in
choose_clusters, ifresult_dfhas less than 3 pathways, do not perform clustering.
+ -
+
run_pathfindRchecks whether the output directory (output_dir) already exists and if it exists, now appends “(1)” tooutput_dirand displays a warning message. This was implemented to prevent writing over existing results.
+ - in run
run_pathfindR, recursive creation for the output directory (output_dir) is now supported.
+ - in run
run_pathfindR, if no pathways are found, the function returns an empty data frame instead of raising an error.
+
pathfindR 1.2
+Major Changes
+Implemented the (per subject) pathway scoring function
calculate_pw_scoresand the function to plot the heatmap of pathway scores per subjectplot_scores.
+Added the
autoparameter tochoose_clusters. Whenauto == TRUE(default), the function chooses the optimal number of clusterskautomatically, as the value which maximizes the average silhouette width. It then returns a data frame with the cluster assignments and the representative/member statuses of each pathway.
+Added the
Fold_Enrichmentcolumn to the resulting data frame ofenrichment, and as a corollary to the resulting data frame ofrun_pathfindR.
+Added the option
bubbleto plot a bubble chart displaying the enrichment results inrun_pathfindRusing the helper functionenrichment_chart. To plot the bubble chart setbubble = TRUEinrun_pathfindRor useenrichment_chart(your_result_df).
+
Minor Changes and Bug Fixes
+Add the parameter
silent_optiontorun_pathfindR. Whensilent_option == TRUE(default), the console outputs during active subnetwork search are printed to a file named “console_out.txt”. Ifsilent_option == FALSE, the output is printed on the screen. Default was set toTRUEbecause multiple console outputs are simultaneously printed when running in parallel.
+Added the
list_active_snw_genesparameter torun_pathfindR. Whenlist_active_snw_genes == TRUE, the function adds the columnnon_DEG_Active_Snw_Genes, which reports the non-DEG active subnetwork genes for the active subnetwork which was enriched for the given pathway with the lowest p value.
+Added the data
RA_clustered, which is the example output of the clustering workflow.
+In the function,
run_pathfindRadded the option to specify the argumentoutput_dirwhich specifies the directory to be created under the current working directory for storing the result HTML files.output_diris “pathfindR_Results” by default.
+run_pathfindRnow checks whether the output directory (output_dir) already exists and if it exists, stops and displays an error message. This was implemented to prevent writing over existing results.
+genes_table.htmlnow contains a second table displaying the input gene symbols for which there were no interactions in the PIN.
+
pathfindR 1.1
+Major changes
+- Added the
gene_setsoption inrun_pathfindRto chose between different gene sets. Available gene sets areKEGG,Reactome,BioCartaand Gene Ontology gene sets (GO-BP,GO-CCandGO-MF)
+ -
+
cluster_pathwaysautomatically recognizes the ID type and chooses the gene sets accordingly
+
Minor Changes and Bug Fixes
+- Fixed issue regarding p values < 1e-13. No active subnetworks were found when there were p values < 1e-13. These are now changed to 1e-13 in the function
input_processing+
+ - In
input_processing, genes for which no interactions are found in the PIN are now removed before active subnetwork search
+ - Duplicated gene symbols no longer raise an error. If there are duplicated symbols, the lowest p value is chosen for each gene symbol in the function
input_processing+
+ - To prevent the formation of nested folders, by default and on errors, the function
run_pathfindRreturns to the user’s working directory.
+ - Citation information are now provided for our BioRxiv pre-print +
Create UpSet Plot of Enriched Terms
- Source:R/visualization.R
+ Source: R/visualization.R
UpSet_plot.RdCreate UpSet Plot of Enriched Terms
Arguments
-- result_df + + +
- result_df
A dataframe of pathfindR results that must contain the following columns:
- Term_Description
Description of the enriched term (necessary if
use_description = TRUE)
@@ -118,7 +120,7 @@
Arguments
-- genes_df +
- genes_df
the input data that was used with
run_pathfindR. It must be a data frame with 3 columns:Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (optional)
@@ -126,43 +128,41 @@
Arguments
The change values in this data frame are used to color the affected genes
-- num_terms +
- num_terms
Number of top enriched terms to use while creating the plot. Set to
NULLto use all enriched terms (default = 10)
-- method +
- method
the option for producing the plot. Options include 'heatmap', 'boxplot' and 'barplot'. (default = 'heatmap')
-- use_description +
- use_description
Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default =
FALSE)
-- low +
- low
a string indicating the color of 'low' values in the coloring gradient (default = 'green')
-- mid +
- mid
a string indicating the color of 'mid' values in the coloring gradient (default = 'black')
-- high +
- high
a string indicating the color of 'high' values in the coloring gradient (default = 'red')
-- ... +
- ...
additional arguments for
input_processing(used ifgenes_dfis provided)
Value
- - -UpSet plots are plots of the intersections of sets as a matrix. This +
UpSet plots are plots of the intersections of sets as a matrix. This
function creates a ggplot object of an UpSet plot where the x-axis is the
UpSet plot of intersections of enriched terms. By default (i.e.
method = 'heatmap') the main plot is a heatmap of genes at the
@@ -192,16 +192,16 @@
Examples
Wrapper for Active Subnetwork Search + Enrichment over Single/Multiple Iteration(s)
- Source:R/utility.R
+ Source: R/utility.R
active_snw_enrichment_wrapper.RdWrapper for Active Subnetwork Search + Enrichment over Single/Multiple Itera
Arguments
- - input_processed
+
+
+- input_processed
processed input data frame
- pin_path
+- pin_path
path/to/PIN/file
- gset_list
+- gset_list
list for gene sets
- enrichment_threshold
+- enrichment_threshold
adjusted-p value threshold used when filtering
enrichment results (default = 0.05)
- list_active_snw_genes
+- list_active_snw_genes
boolean value indicating whether or not to report
the non-significant active subnetwork genes for the active subnetwork which was enriched for
the given term with the lowest p value (default = FALSE)
- adj_method
+- adj_method
correction method to be used for adjusting p-values.
(default = 'bonferroni')
- search_method
+- search_method
algorithm to use when performing active subnetwork
search. Options are greedy search (GR), simulated annealing (SA) or genetic
algorithm (GA) for the search (default = 'GR').
- disable_parallel
+- disable_parallel
boolean to indicate whether to disable parallel runs
via foreach (default = FALSE)
- use_all_positives
+- use_all_positives
if TRUE: in GA, adds an individual with all positive
nodes. In SA, initializes candidate solution with all positive nodes. (default = FALSE)
- iterations
+- iterations
number of iterations for active subnetwork search and
enrichment analyses (Default = 10)
- n_processes
+- n_processes
optional argument for specifying the number of processes
used by foreach. If not specified, the function determines this
automatically (Default == NULL. Gets set to 1 for Genetic Algorithm)
- score_quan_thr
+- score_quan_thr
active subnetwork score quantile threshold. Must be
between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
- sig_gene_thr
+- sig_gene_thr
threshold for the minimum proportion of significant genes in
the subnetwork (Default = 0.02) If the number of genes to use as threshold is
calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number
is set to 2
- saTemp0
+- saTemp0
Initial temperature for SA (default = 1.0)
- saTemp1
+- saTemp1
Final temperature for SA (default = 0.01)
- saIter
+- saIter
Iteration number for SA (default = 10000)
- gaPop
+- gaPop
Population size for GA (default = 400)
- gaIter
+- gaIter
Iteration number for GA (default = 200)
- gaThread
+- gaThread
Number of threads to be used in GA (default = 5)
- gaCrossover
+- gaCrossover
Applies crossover with the given probability in GA (default = 1, i.e. always perform crossover)
- gaMut
+- gaMut
For GA, applies mutation with given mutation rate (default = 0, i.e. mutation off)
- grMaxDepth
+- grMaxDepth
Sets max depth in greedy search, 0 for no limit (default = 1)
- grSearchDepth
+- grSearchDepth
Search depth in greedy search (default = 1)
- grOverlap
+- grOverlap
Overlap threshold for results of greedy search (default = 0.5)
- grSubNum
+- grSubNum
Number of subnetworks to be presented in the results (default = 1000)
- silent_option
+- silent_option
boolean value indicating whether to print the messages
to the console (FALSE) or not (TRUE, this will print to a temp. file) during
active subnetwork search (default = TRUE). This option was added because
@@ -239,9 +241,7 @@
Arguments
Value
-
-
-Data frame of combined pathfindR enrichment results
+ Data frame of combined pathfindR enrichment results
Arguments
-- input_processed + + +
- input_processed
processed input data frame
-- pin_path +
- pin_path
path/to/PIN/file
-- gset_list +
- gset_list
list for gene sets
-- enrichment_threshold +
- enrichment_threshold
adjusted-p value threshold used when filtering enrichment results (default = 0.05)
-- list_active_snw_genes +
- list_active_snw_genes
boolean value indicating whether or not to report the non-significant active subnetwork genes for the active subnetwork which was enriched for the given term with the lowest p value (default =
FALSE)
-- adj_method +
- adj_method
correction method to be used for adjusting p-values. (default = 'bonferroni')
-- search_method +
- search_method
algorithm to use when performing active subnetwork search. Options are greedy search (GR), simulated annealing (SA) or genetic algorithm (GA) for the search (default = 'GR').
-- disable_parallel +
- disable_parallel
boolean to indicate whether to disable parallel runs via
foreach(default = FALSE)
-- use_all_positives +
- use_all_positives
if TRUE: in GA, adds an individual with all positive nodes. In SA, initializes candidate solution with all positive nodes. (default = FALSE)
-- iterations +
- iterations
number of iterations for active subnetwork search and enrichment analyses (Default = 10)
-- n_processes +
- n_processes
optional argument for specifying the number of processes used by foreach. If not specified, the function determines this automatically (Default == NULL. Gets set to 1 for Genetic Algorithm)
-- score_quan_thr +
- score_quan_thr
active subnetwork score quantile threshold. Must be between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
-- sig_gene_thr +
- sig_gene_thr
threshold for the minimum proportion of significant genes in the subnetwork (Default = 0.02) If the number of genes to use as threshold is calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number is set to 2
-- saTemp0 +
- saTemp0
Initial temperature for SA (default = 1.0)
-- saTemp1 +
- saTemp1
Final temperature for SA (default = 0.01)
-- saIter +
- saIter
Iteration number for SA (default = 10000)
-- gaPop +
- gaPop
Population size for GA (default = 400)
-- gaIter +
- gaIter
Iteration number for GA (default = 200)
-- gaThread +
- gaThread
Number of threads to be used in GA (default = 5)
-- gaCrossover +
- gaCrossover
Applies crossover with the given probability in GA (default = 1, i.e. always perform crossover)
-- gaMut +
- gaMut
For GA, applies mutation with given mutation rate (default = 0, i.e. mutation off)
-- grMaxDepth +
- grMaxDepth
Sets max depth in greedy search, 0 for no limit (default = 1)
-- grSearchDepth +
- grSearchDepth
Search depth in greedy search (default = 1)
-- grOverlap +
- grOverlap
Overlap threshold for results of greedy search (default = 0.5)
-- grSubNum +
- grSubNum
Number of subnetworks to be presented in the results (default = 1000)
-- silent_option +
- silent_option
boolean value indicating whether to print the messages to the console (FALSE) or not (TRUE, this will print to a temp. file) during active subnetwork search (default = TRUE). This option was added because @@ -239,9 +241,7 @@
Arguments
Value
- - -Data frame of combined pathfindR enrichment results
+Data frame of combined pathfindR enrichment results
Value
Perform Active Subnetwork Search
- Source:R/active_snw_search.R
+ Source: R/active_snw_search.R
active_snw_search.RdPerform Active Subnetwork Search
Arguments
-- input_for_search + + +
- input_for_search
input the input data that active subnetwork search uses. The input must be a data frame containing at least these 2 columns:
- GENE
Gene Symbol
@@ -124,113 +126,111 @@
Arguments
-- pin_name_path +
- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name, must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
-- snws_file +
- snws_file
name for active subnetwork search output data without file extension (default = 'active_snws')
-- dir_for_parallel_run +
- dir_for_parallel_run
(previously created) directory for a parallel run iteration. Used in the wrapper function (see ?run_pathfindR) (Default = NULL)
-- score_quan_thr +
- score_quan_thr
active subnetwork score quantile threshold. Must be between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
-- sig_gene_thr +
- sig_gene_thr
threshold for the minimum proportion of significant genes in the subnetwork (Default = 0.02) If the number of genes to use as threshold is calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number is set to 2
-- search_method +
- search_method
algorithm to use when performing active subnetwork search. Options are greedy search (GR), simulated annealing (SA) or genetic algorithm (GA) for the search (default = 'GR').
-- seedForRandom +
- seedForRandom
seed for reproducibility while running the java modules (applies for GR and SA)
-- silent_option +
- silent_option
boolean value indicating whether to print the messages to the console (FALSE) or not (TRUE, this will print to a temp. file) during active subnetwork search (default = TRUE). This option was added because during parallel runs, the console messages get disorderly printed.
-- use_all_positives +
- use_all_positives
if TRUE: in GA, adds an individual with all positive nodes. In SA, initializes candidate solution with all positive nodes. (default = FALSE)
-- geneInitProbs +
- geneInitProbs
For SA and GA, probability of adding a gene in initial solution (default = 0.1)
-- saTemp0 +
- saTemp0
Initial temperature for SA (default = 1.0)
-- saTemp1 +
- saTemp1
Final temperature for SA (default = 0.01)
-- saIter +
- saIter
Iteration number for SA (default = 10000)
-- gaPop +
- gaPop
Population size for GA (default = 400)
-- gaIter +
- gaIter
Iteration number for GA (default = 200)
-- gaThread +
- gaThread
Number of threads to be used in GA (default = 5)
-- gaCrossover +
- gaCrossover
Applies crossover with the given probability in GA (default = 1, i.e. always perform crossover)
-- gaMut +
- gaMut
For GA, applies mutation with given mutation rate (default = 0, i.e. mutation off)
-- grMaxDepth +
- grMaxDepth
Sets max depth in greedy search, 0 for no limit (default = 1)
-- grSearchDepth +
- grSearchDepth
Search depth in greedy search (default = 1)
-- grOverlap +
- grOverlap
Overlap threshold for results of greedy search (default = 0.5)
-- grSubNum +
- grSubNum
Number of subnetworks to be presented in the results (default = 1000)
Value
- - -A list of genes in every identified active subnetwork that has a score greater than +
A list of genes in every identified active subnetwork that has a score greater than the `score_quan_thr`th quantile and that has at least `sig_gene_thr` affected genes.
Examples
Annotate the Affected Genes in the Provided Enriched Terms
- Source:R/utility.R
+ Source: R/utility.R
annotate_term_genes.RdAnnotate the Affected Genes in the Provided Enriched Terms
Arguments
-- result_df + + +
- result_df
data frame of enrichment results. The only must-have column is 'ID'.
-- input_processed +
- input_processed
input data processed via
input_processing
-- genes_by_term +
- genes_by_term
List that contains genes for each gene set. Names of this list are gene set IDs (default = kegg_genes)
Value
- - -The original data frame with two additional columns:
- Up_regulated +
- Up_regulated
the up-regulated genes in the input involved in the given term's gene set, comma-separated
- Down_regulated @@ -143,16 +143,16 @@
The original data frame with two additional columns:
Examples
Check Java Version
Arguments
-- version + + +
- version
character vector containing the output of 'java -version'. If NULL, result of
fetch_java_versionis used (default = NULL)
Value
- - -only parses and checks whether the java version is >= 1.8
+only parses and checks whether the java version is >= 1.8
Details
@@ -116,16 +116,16 @@Details
Cluster Enriched Terms
Arguments
-- enrichment_res + + +
- enrichment_res
data frame of pathfindR enrichment results. Must-have columns are 'Term_Description' (if
use_description = TRUE) or 'ID' (ifuse_description = FALSE), 'Down_regulated', and 'Up_regulated'. @@ -103,29 +105,29 @@Arguments
provided.
-- method +
- method
Either 'hierarchical' or 'fuzzy'. Details of clustering are provided in the corresponding functions
hierarchical_term_clustering, andfuzzy_term_clustering
-- plot_clusters_graph +
- plot_clusters_graph
boolean value indicate whether or not to plot the graph diagram of clustering results (default = TRUE)
-- use_description +
- use_description
Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default =
FALSE)
-- use_active_snw_genes +
- use_active_snw_genes
boolean to indicate whether or not to use non-input active subnetwork genes in the calculation of kappa statistics (default = FALSE, i.e. only use affected genes)
-- ... +
- ...
additional arguments for
hierarchical_term_clustering,fuzzy_term_clusteringandcluster_graph_vis. See documentation of these functions for more details.
@@ -133,9 +135,7 @@
Arguments
Value
- - -a data frame of clustering results. For 'hierarchical', the cluster +
a data frame of clustering results. For 'hierarchical', the cluster assignments (Cluster) and whether the term is representative of its cluster (Status) is added as columns. For 'fuzzy', terms that are in multiple clusters are provided for each cluster. The cluster assignments (Cluster) @@ -177,16 +177,16 @@
Examples
Graph Visualization of Clustered Enriched Terms
- Source:R/clustering.R
+ Source: R/clustering.R
cluster_graph_vis.RdGraph Visualization of Clustered Enriched Terms
Arguments
-- clu_obj + + +
- clu_obj
clustering result (either a matrix obtained via
hierarchical_term_clusteringorfuzzy_term_clustering`fuzzy_term_clustering` or a vector obtained via `hierarchical_term_clustering`)
-- kappa_mat +
- kappa_mat
matrix of kappa statistics (output of
create_kappa_matrix)
-- enrichment_res +
- enrichment_res
data frame of pathfindR enrichment results. Must-have columns are 'Term_Description' (if
use_description = TRUE) or 'ID' (ifuse_description = FALSE), 'Down_regulated', and 'Up_regulated'. @@ -114,29 +116,27 @@Arguments
provided.
-- kappa_threshold +
- kappa_threshold
threshold for kappa statistics, defining strong relation (default = 0.35)
-- use_description +
- use_description
Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default =
FALSE)
-- vertex.label.cex +
- vertex.label.cex
font size for vertex labels; it is interpreted as a multiplication factor of some device-dependent base font size (default = 0.7)
-- vertex.size.scaling +
- vertex.size.scaling
scaling factor for the node size (default = 2.5)
Value
- - -Plots a graph diagram of clustering results. Each node is an enriched term +
Plots a graph diagram of clustering results. Each node is an enriched term from `enrichment_res`. Size of node corresponds to -log(lowest_p). Thickness of the edges between nodes correspond to the kappa statistic between the two terms. Color of each node corresponds to distinct clusters. For fuzzy @@ -145,9 +145,9 @@
Value
Examples
-if (FALSE) {
+ if (FALSE) { # \dontrun{
cluster_graph_vis(clu_obj, kappa_mat, enrichment_res)
-}
+} # }
Examples
Color hsa KEGG pathway
- Source:R/visualization.R
+ Source: R/visualization.R
color_kegg_pathway.RdColor hsa KEGG pathway
Arguments
-- pw_id + + +
- pw_id
hsa KEGG pathway id (e.g. hsa05012)
-- change_vec +
- change_vec
vector of change values, names should be hsa KEGG gene ids
-- scale_vals +
- scale_vals
should change values be scaled? (default =
TRUE)
-- node_cols +
- node_cols
low, middle and high color values for coloring the pathway nodes (default =
NULL). Ifnode_cols=NULL, the low, middle and high color are set as 'green', 'gray' and 'red'. If all change values are 1e6 (in case no @@ -114,26 +116,24 @@Arguments
input_processing), only one color ('#F38F18' if NULL) is used.
-- legend.position +
- legend.position
the default position of legends ("none", "left", "right", "bottom", "top", "inside")
Value
- - -a ggplot object containing the colored KEGG pathway diagram visualization
+a ggplot object containing the colored KEGG pathway diagram visualization
Examples
-Examples
Combine 2 pathfindR Results
- Source:R/comparison.R
+ Source: R/comparison.R
combine_pathfindR_results.RdCombine 2 pathfindR Results
Arguments
-Value
- - -Data frame of combined pathfindR enrichment results. Columns are:
- ID +
- ID
ID of the enriched term
- Term_Description @@ -160,8 +160,8 @@
Data frame of combined pathfindR enrichment results. Columns are:
Value
Examples
combined_results <- combine_pathfindR_results(example_pathfindR_output, example_comparison_output)
-#> You may run `combined_results_graph()` to create visualizations of combined term-gene graphs of selected terms
+#> You may run `combined_results_graph()` to create visualizations of combined term-gene graphs of selected terms
Examples
Combined Results Graph
Arguments
-- combined_df + + +
- combined_df
Data frame of combined pathfindR enrichment results
-- selected_terms +
- selected_terms
the vector of selected terms for creating the graph (either IDs or term descriptions). If set to
'common', all of the common terms are used. (default = 'common')
-- use_description +
- use_description
Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default =
FALSE)
-- layout +
- layout
The type of layout to create (see
ggraphfor details. Default = 'stress')
-- node_size +
- node_size
Argument to indicate whether to use number of significant genes ('num_genes') or the -log10(lowest p value) ('p_val') for adjusting the node sizes (default = 'num_genes')
Value
- - -a ggraph object containing the combined term-gene graph.
+
a ggraph object containing the combined term-gene graph.
Each node corresponds to an enriched term (orange if common, different shades of blue otherwise),
an up-regulated gene (green), a down-regulated gene (red) or
a conflicting (i.e. up in one analysis, down in the other or vice versa) gene
@@ -155,16 +155,16 @@
Examples
Configure Output Directory Name
Arguments
-Value
- - -/path/to/output/dir
+/path/to/output/dir
Value
Create HTML Report of pathfindR Results
- Source:R/utility.R
+ Source: R/utility.R
create_HTML_report.RdCreate HTML Report of pathfindR Results
Arguments
-- input + + +
- input
the input data that pathfindR uses. The input must be a data frame with three columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (OPTIONAL)
@@ -96,15 +98,15 @@
Arguments
-- input_processed +
- input_processed
processed input data frame
-- final_res +
- final_res
final pathfindR result data frame
-- dir_for_report +
- dir_for_report
directory to render the report in
Arguments
Create Kappa Statistics Matrix
- Source:R/clustering.R
+ Source: R/clustering.R
create_kappa_matrix.RdCreate Kappa Statistics Matrix
Arguments
-- enrichment_res + + +
- enrichment_res
data frame of pathfindR enrichment results. Must-have columns are 'Term_Description' (if
use_description = TRUE) or 'ID' (ifuse_description = FALSE), 'Down_regulated', and 'Up_regulated'. @@ -100,12 +102,12 @@Arguments
provided.
-- use_description +
- use_description
Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default =
FALSE)
-- use_active_snw_genes +
- use_active_snw_genes
boolean to indicate whether or not to use non-input active subnetwork genes in the calculation of kappa statistics (default = FALSE, i.e. only use affected genes)
@@ -113,9 +115,7 @@
Arguments
Value
- - -a matrix of kappa statistics between each term in the +
a matrix of kappa statistics between each term in the enrichment results.
Examples
Perform Enrichment Analysis for a Single Gene Set
- Source:R/enrichment.R
+ Source: R/enrichment.R
enrichment.RdPerform Enrichment Analysis for a Single Gene Set
Arguments
-- input_genes + + +
- input_genes
The set of gene symbols to be used for enrichment analysis. In the scope of this package, these are genes that were identified for an active subnetwork
-- genes_by_term +
- genes_by_term
List that contains genes for each gene set. Names of this list are gene set IDs (default = kegg_genes)
-- term_descriptions +
- term_descriptions
Vector that contains term descriptions for the gene sets. Names of this vector are gene set IDs (default = kegg_descriptions)
-- adj_method +
- adj_method
correction method to be used for adjusting p-values. (default = 'bonferroni')
-- enrichment_threshold +
- enrichment_threshold
adjusted-p value threshold used when filtering enrichment results (default = 0.05)
-- sig_genes_vec +
- sig_genes_vec
vector of significant gene symbols. In the scope of this package, these are the input genes that were used for active subnetwork search
-- background_genes +
- background_genes
vector of background genes. In the scope of this package, the background genes are taken as all genes in the PIN (see
enrichment_analyses)
@@ -135,9 +137,7 @@
Arguments
Value
- - -A data frame that contains enrichment results
+A data frame that contains enrichment results
See also
@@ -174,16 +174,16 @@Examples
Perform Enrichment Analyses on the Input Subnetworks
- Source:R/enrichment.R
+ Source: R/enrichment.R
enrichment_analyses.RdPerform Enrichment Analyses on the Input Subnetworks
Arguments
-- snws + + +
- snws
a list of subnetwork genes (i.e., vectors of genes for each subnetwork)
-- sig_genes_vec +
- sig_genes_vec
vector of significant gene symbols. In the scope of this package, these are the input genes that were used for active subnetwork search
-- pin_name_path +
- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name, must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
-- genes_by_term +
- genes_by_term
List that contains genes for each gene set. Names of this list are gene set IDs (default = kegg_genes)
-- term_descriptions +
- term_descriptions
Vector that contains term descriptions for the gene sets. Names of this vector are gene set IDs (default = kegg_descriptions)
-- adj_method +
- adj_method
correction method to be used for adjusting p-values. (default = 'bonferroni')
-- enrichment_threshold +
- enrichment_threshold
adjusted-p value threshold used when filtering enrichment results (default = 0.05)
-- list_active_snw_genes +
- list_active_snw_genes
boolean value indicating whether or not to report the non-significant active subnetwork genes for the active subnetwork which was enriched for the given term with the lowest p value (default =
FALSE)
@@ -140,9 +142,7 @@
Arguments
Value
- - -a dataframe of combined enrichment results. Columns are:
- ID +
- ID
ID of the enriched term
- Term_Description @@ -191,16 +191,16 @@
a dataframe of combined enrichment results. Columns are:
Examples
Create Bubble Chart of Enrichment Results
- Source:R/visualization.R
+ Source: R/visualization.R
enrichment_chart.RdCreate Bubble Chart of Enrichment Results
Arguments
-- result_df + + +
- result_df
a data frame that must contain the following columns:
- Term_Description
Description of the enriched term
@@ -119,7 +121,7 @@
Arguments
-- top_terms +
- top_terms
number of top terms (according to the 'lowest_p' column) to plot (default = 10). If
plot_by_cluster = TRUE, selects the toptop_termsterms per each cluster. Settop_terms = NULLto plot @@ -127,18 +129,18 @@Arguments
all terms are plotted.
-- plot_by_cluster +
- plot_by_cluster
boolean value indicating whether or not to group the enriched terms by cluster (works if
result_dfcontains a 'Cluster' column).
-- num_bubbles +
- num_bubbles
number of sizes displayed in the legend
# genes(Default = 4)
-- even_breaks +
- even_breaks
whether or not to set even breaks for the number of sizes displayed in the legend
# genes. IfTRUE(default), sets equal breaks and the number of displayed bubbles may be different than the @@ -148,9 +150,7 @@Arguments
Value
- - -a ggplot2 object containing the bubble chart.
+
a ggplot2 object containing the bubble chart.
The x-axis corresponds to fold enrichment values while the y-axis indicates
the enriched terms. Size of the bubble indicates the number of significant
genes in the given enriched term. Color indicates the -log10(lowest-p) value.
@@ -177,16 +177,16 @@
Examples
Fetch Gene Set Objects
Arguments
-- gene_sets + + +
- gene_sets
Name of the gene sets to be used for enrichment analysis. Available gene sets are 'KEGG', 'Reactome', 'BioCarta', 'GO-All', 'GO-BP', 'GO-CC', 'GO-MF', 'cell_markers', 'mmu_KEGG' or 'Custom'. @@ -104,21 +106,21 @@
Arguments
must be specified. (Default = 'KEGG')
-- min_gset_size +
- min_gset_size
minimum number of genes a term must contain (default = 10)
-- max_gset_size +
- max_gset_size
maximum number of genes a term must contain (default = 300)
-- custom_genes +
- custom_genes
a list containing the genes involved in each custom term. Each element is a vector of gene symbols located in the given custom term. Names should correspond to the IDs of the custom terms.
-- custom_descriptions +
- custom_descriptions
A vector containing the descriptions for each custom term. Names of the vector should correspond to the IDs of the custom terms.
@@ -126,9 +128,7 @@
Arguments
Value
- - -a list containing 2 elements
- genes_by_term +
- genes_by_term
list of vectors of genes contained in each term
- term_descriptions @@ -155,16 +155,16 @@
a list containing 2 elements
Examples
Obtain Java Version
Value
- - -character vector containing the output of 'java -version'
+character vector containing the output of 'java -version'
Details
@@ -109,16 +107,16 @@Details
Parse Active Subnetwork Search Output File and Filter the Subnetworks
- Source:R/active_snw_search.R
+ Source: R/active_snw_search.R
filterActiveSnws.RdParse Active Subnetwork Search Output File and Filter the Subnetworks
Arguments
-- active_snw_path + + +
- active_snw_path
path to the output of an Active Subnetwork Search
-- sig_genes_vec +
- sig_genes_vec
vector of significant gene symbols. In the scope of this package, these are the input genes that were used for active subnetwork search
-- score_quan_thr +
- score_quan_thr
active subnetwork score quantile threshold. Must be between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
-- sig_gene_thr +
- sig_gene_thr
threshold for the minimum proportion of significant genes in the subnetwork (Default = 0.02) If the number of genes to use as threshold is calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number @@ -116,9 +118,7 @@
Arguments
Value
- - -A list containing subnetworks: a list of of genes in every
+
A list containing subnetworks: a list of of genes in every
active subnetwork that has a score greater than the score_quan_thrth
quantile and that contains at least sig_gene_thr of significant genes
and scores the score of each filtered active subnetwork
Examples
Heuristic Fuzzy Multiple-linkage Partitioning of Enriched Terms
- Source:R/clustering.R
+ Source: R/clustering.R
fuzzy_term_clustering.RdHeuristic Fuzzy Multiple-linkage Partitioning of Enriched Terms
Arguments
-- kappa_mat + + +
- kappa_mat
matrix of kappa statistics (output of
create_kappa_matrix)
-- enrichment_res +
- enrichment_res
data frame of pathfindR enrichment results. Must-have columns are 'Term_Description' (if
use_description = TRUE) or 'ID' (ifuse_description = FALSE), 'Down_regulated', and 'Up_regulated'. @@ -105,21 +107,19 @@Arguments
provided.
-- kappa_threshold +
- kappa_threshold
threshold for kappa statistics, defining strong relation (default = 0.35)
-- use_description +
- use_description
Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default =
FALSE)
Value
- - -a boolean matrix of cluster assignments. Each row corresponds to an +
a boolean matrix of cluster assignments. Each row corresponds to an enriched term, each column corresponds to a cluster.
Details
Examples
-if (FALSE) {
+ if (FALSE) { # \dontrun{
fuzzy_term_clustering(kappa_mat, enrichment_res)
fuzzy_term_clustering(kappa_mat, enrichment_res, kappa_threshold = 0.45)
-}
+} # }
Examples
Retrieve the Requested Release of Organism-specific BioGRID PIN
- Source:R/data_generation.R
+ Source: R/data_generation.R
get_biogrid_pin.RdRetrieve the Requested Release of Organism-specific BioGRID PIN
Arguments
-- org + + +
- org
organism name. BioGRID naming requires underscores for spaces so 'Homo sapiens' becomes 'Homo_sapiens', 'Mus musculus' becomes 'Mus_musculus' etc. See https://wiki.thebiogrid.org/doku.php/statistics for a full list of available organisms (default = 'Homo_sapiens')
-- path2pin +
- path2pin
the path of the file to save the PIN data. By default, the PIN data is saved in a temporary file
-- release +
- release
the requested BioGRID release (default = 'latest')
Value
- - -the path of the file in which the PIN data was saved. If +
the path of the file in which the PIN data was saved. If
path2pin was not supplied by the user, the PIN data is saved in a
temporary file
Value
Retrieve Organism-specific Gene Sets List
- Source:R/data_generation.R
+ Source: R/data_generation.R
get_gene_sets_list.RdRetrieve Organism-specific Gene Sets List
Arguments
-- source + + +
- source
As of this version, either 'KEGG', 'Reactome' or 'MSigDB' (default = 'KEGG')
-- org_code +
- org_code
(Used for 'KEGG' only) KEGG organism code for the selected organism. For a full list of all available organisms, see https://www.genome.jp/kegg/catalog/org_list.html
-- species +
- species
(Used for 'MSigDB' only) species name, such as Homo sapiens, Mus musculus, etc. See
msigdbr_show_speciesfor all the species available in the msigdbr package (default = 'Homo sapiens')
-- collection +
- collection
(Used for 'MSigDB' only) collection. i.e., H, C1, C2, C3, C4, C5, C6, C7.
-- subcollection +
- subcollection
(Used for 'MSigDB' only) sub-collection, such as CGP, MIR, BP, etc. (default = NULL, i.e. list all gene sets in collection)
Value
- - -A list containing 2 elements:
gene_sets - A list containing the genes involved in each gene set
+ gene_sets - A list containing the genes involved in each gene set
descriptions - A named vector containing the descriptions for each gene set
A list containing 2 elements:
. For 'KEGG' and 'MSigDB', it is possible to choose a specific organism. For a full list of all available KEGG organisms, see https://www.genome.jp/kegg/catalog/org_list.html. @@ -143,16 +143,16 @@
Value
Retrieve Organism-specific KEGG Pathway Gene Sets
- Source:R/data_generation.R
+ Source: R/data_generation.R
get_kegg_gsets.RdRetrieve Organism-specific KEGG Pathway Gene Sets
Arguments
-- org_code + + +
- org_code
KEGG organism code for the selected organism. For a full list of all available organisms, see https://www.genome.jp/kegg/catalog/org_list.html
Value
- - -list containing 2 elements:
gene_sets - A list containing KEGG IDs for the genes involved in each KEGG pathway
+ gene_sets - A list containing KEGG IDs for the genes involved in each KEGG pathway
descriptions - A named vector containing the descriptions for each KEGG pathway
list containing 2 elements:
Value
Retrieve Organism-specific MSigDB Gene Sets
- Source:R/data_generation.R
+ Source: R/data_generation.R
get_mgsigdb_gsets.RdRetrieve Organism-specific MSigDB Gene Sets
Arguments
-- species + + +
- species
species name, such as Homo sapiens, Mus musculus, etc. See
msigdbr_show_speciesfor all the species available in the msigdbr package
-- collection +
- collection
collection. i.e., H, C1, C2, C3, C4, C5, C6, C7.
-- subcollection +
- subcollection
sub-collection, such as CGP, BP, etc. (default = NULL, i.e. list all gene sets in collection)
Value
- - -Retrieves the MSigDB gene sets and returns a list containing 2 elements:
gene_sets - A list containing the genes involved in each of the selected MSigDB gene sets
+ gene_sets - A list containing the genes involved in each of the selected MSigDB gene sets
descriptions - A named vector containing the descriptions for each selected MSigDB gene set
Retrieves the MSigDB gene sets and returns a list containing 2 elements:
Details
Retrieve Organism-specific PIN data
- Source:R/data_generation.R
+ Source: R/data_generation.R
get_pin_file.RdRetrieve Organism-specific PIN data
Arguments
-- source + + +
- source
As of this version, this function is implemented to get data from 'BioGRID' only. This argument (and this wrapper function) was implemented for future utility
-- org +
- org
organism name. BioGRID naming requires underscores for spaces so 'Homo sapiens' becomes 'Homo_sapiens', 'Mus musculus' becomes 'Mus_musculus' etc. See https://wiki.thebiogrid.org/doku.php/statistics for a full list of available organisms (default = 'Homo_sapiens')
-- path2pin +
- path2pin
the path of the file to save the PIN data. By default, the PIN data is saved in a temporary file
-- ... +
- ...
additional arguments for
get_biogrid_pin
Value
- - -the path of the file in which the PIN data was saved. If +
the path of the file in which the PIN data was saved. If
path2pin was not supplied by the user, the PIN data is saved in a
temporary file
Examples
-if (FALSE) {
+ if (FALSE) { # \dontrun{
pin_path <- get_pin_file()
-}
+} # }
Examples
Retrieve Reactome Pathway Gene Sets
- Source:R/data_generation.R
+ Source: R/data_generation.R
get_reactome_gsets.RdRetrieve Reactome Pathway Gene Sets
Value
- - -Gets the latest Reactome pathways gene sets in gmt format. Parses the +
Gets the latest Reactome pathways gene sets in gmt format. Parses the gmt file and returns a list containing 2 elements:
gene_sets - A list containing the genes involved in each Reactome pathway
descriptions - A named vector containing the descriptions for each Reactome pathway
Value
Retrieve Gene Sets from GMT-format File
- Source:R/data_generation.R
+ Source: R/data_generation.R
gset_list_from_gmt.RdRetrieve Gene Sets from GMT-format File
Arguments
-Value
- - -list containing 2 elements:
gene_sets - A list containing the genes involved in each gene set
+ gene_sets - A list containing the genes involved in each gene set
descriptions - A named vector containing the descriptions for each gene set
list containing 2 elements:
Value
Hierarchical Clustering of Enriched Terms
- Source:R/clustering.R
+ Source: R/clustering.R
hierarchical_term_clustering.RdHierarchical Clustering of Enriched Terms
Arguments
-- kappa_mat + + +
- kappa_mat
matrix of kappa statistics (output of
create_kappa_matrix)
-- enrichment_res +
- enrichment_res
data frame of pathfindR enrichment results. Must-have columns are 'Term_Description' (if
use_description = TRUE) or 'ID' (ifuse_description = FALSE), 'Down_regulated', and 'Up_regulated'. @@ -108,37 +110,35 @@Arguments
provided.
-- num_clusters +
- num_clusters
number of clusters to be formed (default =
NULL). IfNULL, the optimal number of clusters is determined as the number which yields the highest average silhouette width.
-- use_description +
- use_description
Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default =
FALSE)
-- clu_method +
- clu_method
the agglomeration method to be used (default = 'average', see
hclust)
-- plot_hmap +
- plot_hmap
boolean to indicate whether to plot the kappa statistics clustering heatmap or not (default = FALSE)
-- plot_dend +
- plot_dend
boolean to indicate whether to plot the clustering dendrogram partitioned into the optimal number of clusters (default = TRUE)
Value
- - -a vector of clusters for each enriched term in the enrichment results.
+a vector of clusters for each enriched term in the enrichment results.
Details
@@ -153,10 +153,10 @@Details
Examples
-if (FALSE) {
+ if (FALSE) { # \dontrun{
hierarchical_term_clustering(kappa_mat, enrichment_res)
hierarchical_term_clustering(kappa_mat, enrichment_res, method = 'complete')
-}
+} # }
Examples
Hypergeometric Distribution-based Hypothesis Testing
- Source:R/enrichment.R
+ Source: R/enrichment.R
hyperg_test.RdHypergeometric Distribution-based Hypothesis Testing
Arguments
-Value
- - -the p-value as determined using the hypergeometric distribution.
+the p-value as determined using the hypergeometric distribution.
Details
@@ -137,16 +137,16 @@Examples
Process Input
Arguments
-- input + + +
- input
the input data that pathfindR uses. The input must be a data frame with three columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (OPTIONAL)
@@ -101,18 +103,18 @@
Arguments
-- p_val_threshold +
- p_val_threshold
the p value threshold to use when filtering the input data frame. Must a numeric value between 0 and 1. (default = 0.05)
-- pin_name_path +
- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name, must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
-- convert2alias +
- convert2alias
boolean to indicate whether or not to convert gene symbols in the input that are not found in the PIN to an alias symbol found in the PIN (default = TRUE) IMPORTANT NOTE: the conversion uses human gene symbols/alias symbols.
@@ -120,9 +122,7 @@
Arguments
Value
- - -This function first filters the input so that all p values are less +
This function first filters the input so that all p values are less than or equal to the threshold. Next, gene symbols that are not found in the PIN are identified. If aliases of these gene symbols are found in the PIN, the symbols are converted to the corresponding aliases. The @@ -173,16 +173,16 @@
Examples
Input Testing
Arguments
-- input + + +
- input
the input data that pathfindR uses. The input must be a data frame with three columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (OPTIONAL)
@@ -96,16 +98,14 @@
Arguments
-- p_val_threshold +
- p_val_threshold
the p value threshold to use when filtering the input data frame. Must a numeric value between 0 and 1. (default = 0.05)
Value
- - -Only checks if the input and the threshold follows the required +
Only checks if the input and the threshold follows the required specifications.
Examples
Check if value is a valid color
Value
- - -TRUE if x is a valid color, otherwise FALSE
+TRUE if x is a valid color, otherwise FALSE
Value
pathfindR: A package for Enrichment Analysis Utilizing Active Subnetworks
- Source:R/pathfindr.R
+ Source: R/pathfindr.R
pathfindr.RdAuthor
Plot the Heatmap of Score Matrix of Enriched Terms per Sample
- Source:R/scoring.R
+ Source: R/scoring.R
plot_scores.RdPlot the Heatmap of Score Matrix of Enriched Terms per Sample
Arguments
-- score_matrix + + +
- score_matrix
Matrix of agglomerated enriched term scores per sample. Columns are samples, rows are enriched terms
-- cases +
- cases
(Optional) A vector of sample names that are cases in the case/control experiment. (default = NULL)
-- label_samples +
- label_samples
Boolean value to indicate whether or not to label the samples in the heatmap plot (default = TRUE)
-- case_title +
- case_title
Naming of the 'Case' group (as in
cases) (default = 'Case')
-- control_title +
- control_title
Naming of the 'Control' group (default = 'Control')
-- low +
- low
a string indicating the color of 'low' values in the coloring gradient (default = 'green')
-- mid +
- mid
a string indicating the color of 'mid' values in the coloring gradient (default = 'black')
-- high +
- high
a string indicating the color of 'high' values in the coloring gradient (default = 'red')
Value
- - -A `ggplot2` object containing the heatmap plot. x-axis indicates +
A `ggplot2` object containing the heatmap plot. x-axis indicates
the samples. y-axis indicates the enriched terms. 'Score' indicates the
score of the term in a given sample. If cases are provided, the plot is
divided into 2 facets, named by case_title and control_title.
Examples
Process Data frame of Protein-protein Interactions
- Source:R/data_generation.R
+ Source: R/data_generation.R
process_pin.RdProcess Data frame of Protein-protein Interactions
Arguments
-Value
- - -processed PIN data frame (removes self-interactions and +
processed PIN data frame (removes self-interactions and duplicated interactions)
Value
Return The Path to Given Protein-Protein Interaction Network (PIN)
- Source:R/utility.R
+ Source: R/utility.R
return_pin_path.RdReturn The Path to Given Protein-Protein Interaction Network (PIN)
Arguments
-- pin_name_path + + +
Value
- - -The absolute path to chosen PIN.
+The absolute path to chosen PIN.
See also
@@ -120,9 +120,9 @@See also
Examples
-if (FALSE) {
+ if (FALSE) { # \dontrun{
pin_path <- return_pin_path('GeneMania')
-}
+} # }
Examples
Wrapper Function for pathfindR - Active-Subnetwork-Oriented Enrichment Workflow
- Source:R/core.R
+ Source: R/core.R
run_pathfindr.RdWrapper Function for pathfindR - Active-Subnetwork-Oriented Enrichment Workf
Arguments
- - input
+
+
+- input
the input data that pathfindR uses. The input must be a data
frame with three columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (OPTIONAL)
Arguments
- gene_sets
+- gene_sets
Name of the gene sets to be used for enrichment analysis.
Available gene sets are 'KEGG', 'Reactome', 'BioCarta', 'GO-All',
'GO-BP', 'GO-CC', 'GO-MF', 'cell_markers', 'mmu_KEGG' or 'Custom'.
@@ -119,73 +121,71 @@
Arguments
must be specified. (Default = 'KEGG')- min_gset_size
+- min_gset_size
minimum number of genes a term must contain (default = 10)
- max_gset_size
+- max_gset_size
maximum number of genes a term must contain (default = 300)
- custom_genes
+- custom_genes
a list containing the genes involved in each custom
term. Each element is a vector of gene symbols located in the given custom
term. Names should correspond to the IDs of the custom terms.
- custom_descriptions
+- custom_descriptions
A vector containing the descriptions for each
custom term. Names of the vector should correspond to the IDs of the custom
terms.
- pin_name_path
+- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name,
must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If
path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
- p_val_threshold
+- p_val_threshold
the p value threshold to use when filtering
the input data frame. Must a numeric value between 0 and 1. (default = 0.05)
- enrichment_threshold
+- enrichment_threshold
adjusted-p value threshold used when filtering
enrichment results (default = 0.05)
- convert2alias
+- convert2alias
boolean to indicate whether or not to convert gene symbols
in the input that are not found in the PIN to an alias symbol found in the PIN
(default = TRUE) IMPORTANT NOTE: the conversion uses human gene symbols/alias symbols.
- plot_enrichment_chart
+- plot_enrichment_chart
boolean value. If TRUE, a bubble chart displaying
the enrichment results is plotted. (default = TRUE)
- output_dir
+- output_dir
the directory to be created where the output and intermediate
files are saved (default = NULL, a temporary directory is used)
- list_active_snw_genes
+- list_active_snw_genes
boolean value indicating whether or not to report
the non-significant active subnetwork genes for the active subnetwork which was enriched for
the given term with the lowest p value (default = FALSE)
- ...
+- ...
additional arguments for active_snw_enrichment_wrapper
Value
-
-
-Data frame of pathfindR enrichment results. Columns are:
- ID
+ Data frame of pathfindR enrichment results. Columns are:
- ID
ID of the enriched term
- Term_Description
@@ -220,8 +220,6 @@ Value
results linked to the visualizations of the enriched terms in addition to
the table of converted gene symbols. This report can be found in
'output_dir/results.html' under the current working directory.
-
-
By default, a bubble chart of top 10 enrichment results are plotted. The x-axis
corresponds to fold enrichment values while the y-axis indicates the enriched
terms. Sizes of the bubbles indicate the number of significant genes in the given terms.
@@ -263,9 +261,9 @@
See also
Examples
- if (FALSE) {
+ if (FALSE) { # \dontrun{
run_pathfindR(example_pathfindR_input)
-}
+} # }
@@ -280,16 +278,16 @@ Examples
Arguments
-- input + + +
- input
the input data that pathfindR uses. The input must be a data frame with three columns:
Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (OPTIONAL)
@@ -111,7 +113,7 @@
Arguments
-- gene_sets +
- gene_sets
Name of the gene sets to be used for enrichment analysis. Available gene sets are 'KEGG', 'Reactome', 'BioCarta', 'GO-All', 'GO-BP', 'GO-CC', 'GO-MF', 'cell_markers', 'mmu_KEGG' or 'Custom'. @@ -119,73 +121,71 @@
Arguments
must be specified. (Default = 'KEGG')
-- min_gset_size +
- min_gset_size
minimum number of genes a term must contain (default = 10)
-- max_gset_size +
- max_gset_size
maximum number of genes a term must contain (default = 300)
-- custom_genes +
- custom_genes
a list containing the genes involved in each custom term. Each element is a vector of gene symbols located in the given custom term. Names should correspond to the IDs of the custom terms.
-- custom_descriptions +
- custom_descriptions
A vector containing the descriptions for each custom term. Names of the vector should correspond to the IDs of the custom terms.
-- pin_name_path +
- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name, must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
-- p_val_threshold +
- p_val_threshold
the p value threshold to use when filtering the input data frame. Must a numeric value between 0 and 1. (default = 0.05)
-- enrichment_threshold +
- enrichment_threshold
adjusted-p value threshold used when filtering enrichment results (default = 0.05)
-- convert2alias +
- convert2alias
boolean to indicate whether or not to convert gene symbols in the input that are not found in the PIN to an alias symbol found in the PIN (default = TRUE) IMPORTANT NOTE: the conversion uses human gene symbols/alias symbols.
-- plot_enrichment_chart +
- plot_enrichment_chart
boolean value. If TRUE, a bubble chart displaying the enrichment results is plotted. (default = TRUE)
-- output_dir +
- output_dir
the directory to be created where the output and intermediate files are saved (default =
NULL, a temporary directory is used)
-- list_active_snw_genes +
- list_active_snw_genes
boolean value indicating whether or not to report the non-significant active subnetwork genes for the active subnetwork which was enriched for the given term with the lowest p value (default =
FALSE)
-- ... +
- ...
additional arguments for
active_snw_enrichment_wrapper
Value
- - -Data frame of pathfindR enrichment results. Columns are:
- ID +
- ID
ID of the enriched term
- Term_Description @@ -220,8 +220,6 @@
Data frame of pathfindR enrichment results. Columns are:
Value
results linked to the visualizations of the enriched terms in addition to the table of converted gene symbols. This report can be found in 'output_dir/results.html' under the current working directory.
-
-
By default, a bubble chart of top 10 enrichment results are plotted. The x-axis corresponds to fold enrichment values while the y-axis indicates the enriched terms. Sizes of the bubbles indicate the number of significant genes in the given terms. @@ -263,9 +261,9 @@
See also
Examples
-if (FALSE) {
+ if (FALSE) { # \dontrun{
run_pathfindR(example_pathfindR_input)
-}
+} # }
Examples
Calculate Agglomerated Scores of Enriched Terms for Each Subject
- Source:R/scoring.R
+ Source: R/scoring.R
score_terms.RdCalculate Agglomerated Scores of Enriched Terms for Each Subject
Arguments
-- enrichment_table + + +
- enrichment_table
a data frame that must contain the 3 columns below:
- Term_Description
Description of the enriched term (necessary if
use_description = TRUE)
@@ -112,43 +114,41 @@
Arguments
-- exp_mat +
- exp_mat
the experiment (e.g., gene expression/methylation) matrix. Columns are samples and rows are genes. Column names must contain sample names and row names must contain the gene symbols.
-- cases +
- cases
(Optional) A vector of sample names that are cases in the case/control experiment. (default = NULL)
-- use_description +
- use_description
Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default =
FALSE)
-- plot_hmap +
- plot_hmap
Boolean value to indicate whether or not to draw the heatmap plot of the scores. (default = TRUE)
-- ... +
- ...
Additional arguments for
plot_scoresfor aesthetics of the heatmap plot
Value
- - -Matrix of agglomerated scores of each enriched term per sample. +
Matrix of agglomerated scores of each enriched term per sample. Columns are samples, rows are enriched terms. Optionally, displays a heatmap of this matrix.
Conceptual Background
- +For an experiment matrix (containing expression, methylation, etc. values), the rows of which are genes and the columns of which are samples, @@ -193,16 +193,16 @@
Examples
Active Subnetwork Search + Enrichment Analysis Wrapper for a Single Iteration
- Source:R/utility.R
+ Source: R/utility.R
single_iter_wrapper.RdActive Subnetwork Search + Enrichment Analysis Wrapper for a Single Iteratio
Arguments
- - i
+
+
+- i
current iteration index (default = NULL)
- dirs
+- dirs
vector of directories for parallel runs
- input_processed
+- input_processed
processed input data frame
- pin_path
+- pin_path
path/to/PIN/file
- score_quan_thr
+- score_quan_thr
active subnetwork score quantile threshold. Must be
between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
- sig_gene_thr
+- sig_gene_thr
threshold for the minimum proportion of significant genes in
the subnetwork (Default = 0.02) If the number of genes to use as threshold is
calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number
is set to 2
- search_method
+- search_method
algorithm to use when performing active subnetwork
search. Options are greedy search (GR), simulated annealing (SA) or genetic
algorithm (GA) for the search (default = 'GR').
- silent_option
+- silent_option
boolean value indicating whether to print the messages
to the console (FALSE) or not (TRUE, this will print to a temp. file) during
active subnetwork search (default = TRUE). This option was added because
during parallel runs, the console messages get disorderly printed.
- use_all_positives
+- use_all_positives
if TRUE: in GA, adds an individual with all positive
nodes. In SA, initializes candidate solution with all positive nodes. (default = FALSE)
- geneInitProbs
+- geneInitProbs
For SA and GA, probability of adding a gene in initial solution (default = 0.1)
- saTemp0
+- saTemp0
Initial temperature for SA (default = 1.0)
- saTemp1
+- saTemp1
Final temperature for SA (default = 0.01)
- saIter
+- saIter
Iteration number for SA (default = 10000)
- gaPop
+- gaPop
Population size for GA (default = 400)
- gaIter
+- gaIter
Iteration number for GA (default = 200)
- gaThread
+- gaThread
Number of threads to be used in GA (default = 5)
- gaCrossover
+- gaCrossover
Applies crossover with the given probability in GA (default = 1, i.e. always perform crossover)
- gaMut
+- gaMut
For GA, applies mutation with given mutation rate (default = 0, i.e. mutation off)
- grMaxDepth
+- grMaxDepth
Sets max depth in greedy search, 0 for no limit (default = 1)
- grSearchDepth
+- grSearchDepth
Search depth in greedy search (default = 1)
- grOverlap
+- grOverlap
Overlap threshold for results of greedy search (default = 0.5)
- grSubNum
+- grSubNum
Number of subnetworks to be presented in the results (default = 1000)
- gset_list
+- gset_list
list for gene sets
- adj_method
+- adj_method
correction method to be used for adjusting p-values.
(default = 'bonferroni')
- enrichment_threshold
+- enrichment_threshold
adjusted-p value threshold used when filtering
enrichment results (default = 0.05)
- list_active_snw_genes
+- list_active_snw_genes
boolean value indicating whether or not to report
the non-significant active subnetwork genes for the active subnetwork which was enriched for
the given term with the lowest p value (default = FALSE)
Arguments
Value
-
-
-Data frame of enrichment results using active subnetwork search results
+ Data frame of enrichment results using active subnetwork search results
Arguments
-- i + + +
- i
current iteration index (default =
NULL)
-- dirs +
- dirs
vector of directories for parallel runs
-- input_processed +
- input_processed
processed input data frame
-- pin_path +
- pin_path
path/to/PIN/file
-- score_quan_thr +
- score_quan_thr
active subnetwork score quantile threshold. Must be between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
-- sig_gene_thr +
- sig_gene_thr
threshold for the minimum proportion of significant genes in the subnetwork (Default = 0.02) If the number of genes to use as threshold is calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number is set to 2
-- search_method +
- search_method
algorithm to use when performing active subnetwork search. Options are greedy search (GR), simulated annealing (SA) or genetic algorithm (GA) for the search (default = 'GR').
-- silent_option +
- silent_option
boolean value indicating whether to print the messages to the console (FALSE) or not (TRUE, this will print to a temp. file) during active subnetwork search (default = TRUE). This option was added because during parallel runs, the console messages get disorderly printed.
-- use_all_positives +
- use_all_positives
if TRUE: in GA, adds an individual with all positive nodes. In SA, initializes candidate solution with all positive nodes. (default = FALSE)
-- geneInitProbs +
- geneInitProbs
For SA and GA, probability of adding a gene in initial solution (default = 0.1)
-- saTemp0 +
- saTemp0
Initial temperature for SA (default = 1.0)
-- saTemp1 +
- saTemp1
Final temperature for SA (default = 0.01)
-- saIter +
- saIter
Iteration number for SA (default = 10000)
-- gaPop +
- gaPop
Population size for GA (default = 400)
-- gaIter +
- gaIter
Iteration number for GA (default = 200)
-- gaThread +
- gaThread
Number of threads to be used in GA (default = 5)
-- gaCrossover +
- gaCrossover
Applies crossover with the given probability in GA (default = 1, i.e. always perform crossover)
-- gaMut +
- gaMut
For GA, applies mutation with given mutation rate (default = 0, i.e. mutation off)
-- grMaxDepth +
- grMaxDepth
Sets max depth in greedy search, 0 for no limit (default = 1)
-- grSearchDepth +
- grSearchDepth
Search depth in greedy search (default = 1)
-- grOverlap +
- grOverlap
Overlap threshold for results of greedy search (default = 0.5)
-- grSubNum +
- grSubNum
Number of subnetworks to be presented in the results (default = 1000)
-- gset_list +
- gset_list
list for gene sets
-- adj_method +
- adj_method
correction method to be used for adjusting p-values. (default = 'bonferroni')
-- enrichment_threshold +
- enrichment_threshold
adjusted-p value threshold used when filtering enrichment results (default = 0.05)
-- list_active_snw_genes +
- list_active_snw_genes
boolean value indicating whether or not to report the non-significant active subnetwork genes for the active subnetwork which was enriched for the given term with the lowest p value (default =
FALSE)
@@ -235,9 +237,7 @@
Arguments
Value
- - -Data frame of enrichment results using active subnetwork search results
+Data frame of enrichment results using active subnetwork search results
Value
Summarize Enrichment Results
- Source:R/enrichment.R
+ Source: R/enrichment.R
summarize_enrichment_results.RdSummarize Enrichment Results
Arguments
-- enrichment_res + + +
- enrichment_res
a dataframe of combined enrichment results. Columns are:
- ID
ID of the enriched term
@@ -111,7 +113,7 @@
Arguments
-- list_active_snw_genes +
- list_active_snw_genes
boolean value indicating whether or not to report the non-significant active subnetwork genes for the active subnetwork which was enriched for the given term with the lowest p value (default =
FALSE)
@@ -119,9 +121,7 @@
Arguments
Value
- - -a dataframe of summarized enrichment results (over multiple iterations). Columns are:
- ID +
- ID
ID of the enriched term
- Term_Description @@ -150,9 +150,9 @@
a dataframe of summarized enrichment results (over multiple iterations). Columns are:
Value
Examples
-if (FALSE) {
+ if (FALSE) { # \dontrun{
summarize_enrichment_results(enrichment_res)
-}
+} # }
Examples
Create Term-Gene Graph
Arguments
-- result_df + + +
- result_df
A dataframe of pathfindR results that must contain the following columns:
- Term_Description
Description of the enriched term (necessary if
use_description = TRUE)
@@ -115,34 +117,32 @@
Arguments
-- num_terms +
- num_terms
Number of top enriched terms to use while creating the graph. Set to
NULLto use all enriched terms (default = 10, i.e. top 10 terms)
-- layout +
- layout
The type of layout to create (see
ggraphfor details. Default = 'stress')
-- use_description +
- use_description
Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default =
FALSE)
-- node_size +
- node_size
Argument to indicate whether to use number of significant genes ('num_genes') or the -log10(lowest p value) ('p_val') for adjusting the node sizes (default = 'num_genes')
-- node_colors +
- node_colors
vector of 3 colors to be used for coloring nodes (colors for term nodes, up, and down, respectively)
Value
- - -a ggraph object containing the term-gene graph.
+
a ggraph object containing the term-gene graph.
Each node corresponds to an enriched term (beige), an up-regulated gene (green)
or a down-regulated gene (red). An edge between a term and a gene indicates
that the given term involves the gene. Size of a term node is proportional
@@ -179,16 +179,16 @@
Examples
Create Terms by Genes Heatmap
- Source:R/visualization.R
+ Source: R/visualization.R
term_gene_heatmap.RdCreate Terms by Genes Heatmap
Arguments
-- result_df + + +
- result_df
A dataframe of pathfindR results that must contain the following columns:
- Term_Description
Description of the enriched term (necessary if
use_description = TRUE)
@@ -119,7 +121,7 @@
Arguments
-- genes_df +
- genes_df
the input data that was used with
run_pathfindR. It must be a data frame with 3 columns:Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (optional)
@@ -127,47 +129,45 @@
Arguments
The change values in this data frame are used to color the affected genes
-- num_terms +
- num_terms
Number of top enriched terms to use while creating the plot. Set to
NULLto use all enriched terms (default = 10)
-- use_description +
- use_description
Boolean argument to indicate whether term descriptions (in the 'Term_Description' column) should be used. (default =
FALSE)
-- low +
- low
a string indicating the color of 'low' values in the coloring gradient (default = 'green')
-- mid +
- mid
a string indicating the color of 'mid' values in the coloring gradient (default = 'black')
-- high +
- high
a string indicating the color of 'high' values in the coloring gradient (default = 'red')
-- legend_title +
- legend_title
legend title (default = 'change')
-- sort_terms_by_p +
- sort_terms_by_p
boolean to indicate whether to sort terms by 'lowest_p' (
TRUE) or by number of genes (FALSE) (default =FALSE)
-- ... +
- ...
additional arguments for
input_processing(used ifgenes_dfis provided)
Value
- - -a ggplot2 object of a heatmap where rows are enriched terms and +
a ggplot2 object of a heatmap where rows are enriched terms and
columns are involved input genes. If genes_df is provided, colors of
the tiles indicate the change values.
Examples
Visualize Human KEGG Pathways
- Source:R/visualization.R
+ Source: R/visualization.R
visualize_KEGG_diagram.RdVisualize Human KEGG Pathways
Arguments
-- kegg_pw_ids + + +
- kegg_pw_ids
KEGG ids of pathways to be colored and visualized
-- input_processed +
- input_processed
input data processed via
input_processing
-- scale_vals +
- scale_vals
should change values be scaled? (default =
TRUE)
-- node_cols +
- node_cols
low, middle and high color values for coloring the pathway nodes (default =
NULL). Ifnode_cols=NULL, the low, middle and high color are set as 'green', 'gray' and 'red'. If all change values are 1e6 (in case no @@ -114,16 +116,14 @@Arguments
input_processing), only one color ('#F38F18' if NULL) is used.
-- legend.position +
- legend.position
the default position of legends ("none", "left", "right", "bottom", "top", "inside")
Value
- - -Creates colored visualizations of the enriched human KEGG pathways +
Creates colored visualizations of the enriched human KEGG pathways and returns them as a list of ggplot objects, named by Term ID.
See also
Examples
-if (FALSE) {
+ if (FALSE) { # \dontrun{
input_processed <- data.frame(
GENE = c("PKLR", "GPI", "CREB1", "INS"),
CHANGE = c(1.5, -2, 3, 5)
)
gg_list <- visualize_KEGG_diagram(c("hsa00010", "hsa04911"), input_processed)
-}
+} # }
Examples
Visualize Active Subnetworks
- Source:R/active_snw_search.R
+ Source: R/active_snw_search.R
visualize_active_subnetworks.RdVisualize Active Subnetworks
Arguments
-- active_snw_path + + +
- active_snw_path
path to the output of an Active Subnetwork Search
-- genes_df +
- genes_df
the input data that was used with
run_pathfindR. It must be a data frame with 3 columns:Gene Symbol (Gene Symbol)
Change value, e.g. log(fold change) (optional)
@@ -109,42 +111,40 @@
Arguments
The change values in this data frame are used to color the affected genes
-- pin_name_path +
- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name, must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
-- num_snws +
- num_snws
number of top subnetworks to be visualized (leave blank if you want to visualize all subnetworks)
-- layout +
- layout
The type of layout to create (see
ggraphfor details. Default = 'stress')
-- score_quan_thr +
- score_quan_thr
active subnetwork score quantile threshold. Must be between 0 and 1 or set to -1 for not filtering. (Default = 0.8)
-- sig_gene_thr +
- sig_gene_thr
threshold for the minimum proportion of significant genes in the subnetwork (Default = 0.02) If the number of genes to use as threshold is calculated to be < 2 (e.g. 50 signif. genes x 0.01 = 0.5), the threshold number is set to 2
-- ... +
- ...
additional arguments for
input_processing
Value
- - -a list of ggplot objects of graph visualizations of identified active +
a list of ggplot objects of graph visualizations of identified active subnetworks. Green nodes are down-regulated genes, reds are up-regulated genes and yellows are non-input genes
Examples
Visualize Interactions of Genes Involved in the Given Enriched Terms
- Source:R/visualization.R
+ Source: R/visualization.R
visualize_term_interactions.RdVisualize Interactions of Genes Involved in the Given Enriched Terms
Arguments
-- result_df + + +
- result_df
Data frame of enrichment results. Must-have columns are: 'Term_Description', 'Up_regulated' and 'Down_regulated'
-- pin_name_path +
- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name, must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
-- show_legend +
- show_legend
Boolean to indicate whether to display the legend (
TRUE) or not (FALSE) (default:TRUE)
Value
- - -list of ggplot objects (named by Term ID) visualizing the interactions of genes involved +
list of ggplot objects (named by Term ID) visualizing the interactions of genes involved
in the given enriched terms (annotated in the result_df) in the PIN used
for enrichment analysis (specified by pin_name_path).
See also
Examples
-if (FALSE) {
+ if (FALSE) { # \dontrun{
result_df <- example_pathfindR_output[1:2, ]
gg_list <- visualize_term_interactions(result_df, pin_name_path = 'IntAct')
-}
+} # }
Examples
Create Diagrams for Enriched Terms
- Source:R/visualization.R
+ Source: R/visualization.R
visualize_terms.RdCreate Diagrams for Enriched Terms
Arguments
-- result_df + + +
- result_df
Data frame of enrichment results. Must-have columns for KEGG human pathway diagrams (
is_KEGG_result = TRUE) are: 'ID' and 'Term_Description'. Must-have columns for the rest are: 'Term_Description', 'Up_regulated' and 'Down_regulated'
-- input_processed +
- input_processed
input data processed via
input_processing, not necessary whenis_KEGG_result = FALSE
-- is_KEGG_result +
- is_KEGG_result
boolean to indicate whether KEGG gene sets were used for enrichment analysis or not (default =
TRUE)
-- pin_name_path +
- pin_name_path
Name of the chosen PIN or absolute/path/to/PIN.sif. If PIN name, must be one of c('Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG', 'mmu_STRING'). If path/to/PIN.sif, the file must comply with the PIN specifications. (Default = 'Biogrid')
-- ... +
- ...
additional arguments for
visualize_KEGG_diagram(used whenis_KEGG_result = TRUE) orvisualize_term_interactions(used whenis_KEGG_result = FALSE)
@@ -125,12 +127,9 @@
Arguments
Value
- - -Depending on the argument is_KEGG_result, creates visualization of
- interactions of genes involved in the list of enriched terms in
result_df. Returns a list of ggplot objects named by Term ID.
Depending on the argument is_KEGG_result, creates visualization of
+ interactions of genes involved in the list of enriched terms in
+ result_df. Returns a list of ggplot objects named by Term ID.
Details
@@ -150,7 +149,7 @@See also
Examples
-if (FALSE) {
+ if (FALSE) { # \dontrun{
input_processed <- data.frame(
GENE = c("PARP1", "NDUFA1", "STX6", "SNAP23"),
CHANGE = c(1.5, -2, 3, 5)
@@ -159,7 +158,7 @@ Examples
gg_list <- visualize_terms(result_df, input_processed)
gg_list2 <- visualize_terms(result_df, is_KEGG_result = FALSE, pin_name_path = 'IntAct')
-}
+} # }