- pISA-tree on GitHub
- Petek, M., Zagorščak, M., Blejec, A. et al. pISA-tree - a data management framework for life science research projects using a standardised directory tree. Sci Data 9, 685 (2022). https://doi.org/10.1038/s41597-022-01805-5
- one or multiple genotypes
- under single and multiple abiotic/biotic stressors
- experiment duration: XY hours, days, ... : time-series experimental design
- tissue: single or multiple
- Omics' strategies:
- Hormonomics
- Transcriptomics
- Proteomics
- Metabolomics
- Phenomics
- Design Phenodata, a master experimental design table describing samples for analysis, prior to sample collection according to good data management practice
- Data preprocessing and overall inspection
-
detection of outliers and faulty measurements
-
data transformation (if needed)
-
interpolation
- point-to-point
- approxfun {stats} - Returns a list of points which linearly interpolate given data points, or a function performing the linear (or constant) interpolation.
- polynomial
- predict {stats} - A generic function for predictions from the results of various model fitting functions
- boxplot.stats {grDevices} - Box Plot Statistics
- mad {BiocGenerics} - Compute the median absolute deviation for a vector
- aq.plot {mvoutlier} - Adjusted quantile plots for multivariate outlier detection
- point-to-point
-
extrapolation
-
imputation
- for qPCR see Baebler, Š., Svalina, M., Petek, M. et al. quantGenius: implementation of a decision support system for qPCR-based gene quantification. BMC Bioinformatics 18, 276 (2017). https://doi.org/10.1186/s12859-017-1688-7
- Statistical analysis of individual omics data layers
- ggplot {ggplot2} - various plots, https://r-graphics.org/chapter-ggplot2
- corr.test {psych} - Find the correlations, sample sizes, and probability values between elements of a matrix or data.frame
- cor.plot {psych} - Create an image plot for a correlation or factor matrix
- pairs.panels {psych} - SPLOM, histograms and correlations for a data matrix
- rcorr {Hmisc} - Matrix of Correlations and P-values
- heatmaply_cor {heatmaply} - Cluster heatmap based on plotly
- corrplot {corrplot} - A visualization of a correlation matrix
- pheatmap {pheatmap} - A function to draw clustered heatmaps
- t_test {rstatix}
- ggdotplot, ggviolin {ggpubr}
- metaMDS {vegan} - Nonmetric Multidimensional Scaling with Stable Solution from Random Starts, Axis Scaling and Species Scores
- {limma} for e.g. non-targeted Proteomics, RNA-seq, ..
- limma::lmFit - Linear Model for Series of Arrays
- limma::makeContrasts - Construct Matrix of Custom Contrasts
- limma::contrasts.fit - Compute Contrasts from Linear Model Fit
- limma::eBayes - Empirical Bayes Statistics for Differential Expression
- limma::decideTests - Multiple Testing Across Genes and Contrasts
- limma::topTable - Table of Top Genes from Linear Model Fit
- Correlation based network inference within each omics level
- Integration across different omics datasets
- Canonical Correlation Analysis
- N-Integration Discriminant Analysis with DIABLO
- {mixOmics}
- block.splsda {mixOmics} N-integration and feature selection with Projection to Latent Structures models (PLS) with sparse Discriminant Analysis
- plotDiablo {mixOmics} Graphical output for the DIABLO framework
- plotVar {mixOmics} Plot of Variables
- plotIndiv {mixOmics} Plot of Individuals (Experimental Units)
- plotArrow {mixOmics} Arrow sample plot
- circosPlot {mixOmics} circosPlot for DIABLO
- cimDiablo {mixOmics} Clustered Image Maps (CIMs) ("heat maps") for DIABLO
- network {mixOmics} Relevance Network for (r)CCA and (s)PLS regression
- {mixOmics}
- Leave-One-Out graphs
- Integration of data with prior knowledge
- Data is expected to be arranged within data management framework, with complete and descriptive metadata files, including Phenodata file.
- 'Omics files are expected to be preprocessed (see suggestions in Step 2).
- Minimal input files can be found within './input' directory.
- For Step 3: Statistical analysis of individual omics data layers run script 01_Step3.Rmd
- For Step 4: Correlation based network inference within/between each omics level run script 02_Step4.Rmd
- For Step 5: Integration across different omics datasets run script 03_Step5.Rnw
For more info see multiOmics_data_analysis_Protocol