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server.R
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server.R
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# Asc-Seurat
# Version 2.2.1
set.seed(1407)
options(shiny.sanitize.errors = FALSE)
options(max.print=100)
# CRAN
suppressMessages( require(tidyverse) )
suppressMessages( require(Seurat) )
suppressMessages( require(SeuratObject) )
suppressMessages( require(patchwork) )
suppressMessages( require(ggplot2) )
suppressMessages( require(circlize) )
suppressMessages( require(reactable) )
suppressMessages( require(sctransform) )
suppressMessages( require(shiny) )
suppressMessages( require(shinyWidgets) )
suppressMessages( require(shinyFeedback) )
suppressMessages( require(rclipboard) )
suppressMessages( require(future) )
suppressMessages( require(ggthemes) )
suppressMessages( require(shinycssloaders) )
suppressMessages( require(DT) )
suppressMessages( require(dplyr) )
suppressMessages( require(hdf5r) )
suppressMessages( require(scales) )
suppressMessages( require(utils) )
suppressMessages( require(vroom) )
# Bioconductor
suppressMessages( require(ComplexHeatmap) )
suppressMessages( require(SingleCellExperiment) )
suppressMessages( require(slingshot) )
suppressMessages( require(multtest) )
suppressMessages( require(biomaRt) )
suppressMessages( require(topGO) )
# dynverse packages
suppressMessages( require(dynplot) )
suppressMessages( require(dynwrap) )
suppressMessages( require(dynfeature) )
## New packages in version 2.1
suppressMessages( require(glmGamPoi) ) # Bioconductor
##
if (dir.exists('/app/user_work')) {
source("/app/R/feature_plot.R")
source("/app/R/adapted_slingshot.R")
source("/app/R/biomart_section.R")
source("/app/R/lognormalization_function.R")
source("/app/R/SCTransform_function.R")
source("/app/R/server_functions.R")
source("/app/R/functions_load_markers_list.R")
source("/app/R/finding_markers.R")
source("/app/R/improved_dot_and_violin_plots.R")
source("/app/R/integration_lognorm.R")
source("/app/R/integration_SCTransform.R")
} else {
source("R/feature_plot.R")
source("R/adapted_slingshot.R")
source("R/biomart_section.R")
source("R/lognormalization_function.R")
source("R/SCTransform_function.R")
source("R/server_functions.R")
source("R/functions_load_markers_list.R")
source("R/finding_markers.R")
source("R/improved_dot_and_violin_plots.R")
source("R/integration_lognorm.R")
source("R/integration_SCTransform.R")
}
###########################
### Load phytozome mart ###
###########################
phytozome_mart <- new("Mart",
biomart = "phytozome_mart",
vschema = "zome_mart",
host = "https://phytozome.jgi.doe.gov:443/biomart/martservice")
function(input, output, session) {
if (dir.exists('/app/user_work')) {
setwd('/app/user_work')
}
#####################################
###### Tab 1 - Clustering ######
#####################################
output$select_sample_tab1 = renderUI({
dir_list <- list.dirs('./data', recursive=FALSE)
div(class = "option-group",
shinyWidgets::pickerInput(
inputId = "sample_folder_tab1",
label = "Select the sample to use",
choices = sort(dir_list),
multiple = FALSE,
options = list(`actions-box` = TRUE)
))
})
output$select_sample_tab1_rds_ui <- renderUI({
rds_list <- list.files('./RDS_files/', pattern = "*.rds")
div(class = "option-group",
shinyWidgets::pickerInput(
inputId = "select_sample_tab1_rds",
label = "Select the file containing the data",
choices = sort(rds_list),
multiple = FALSE,
options = list(`actions-box` = TRUE)
))
})
single_cell_data_reac <- eventReactive( input$load_10X, {
shinyFeedback::feedbackWarning("min_cells", is.na(input$min_cells), "Required value")
shinyFeedback::feedbackWarning("min_features", is.na(input$min_features), "Required value")
shinyFeedback::feedbackWarning("mito_regex", !shiny::isTruthy(input$mito_regex), "Required value")
req(input$min_cells)
req(input$min_features)
req(input$mito_regex)
showNotification("Loading the data",
duration = NULL,
id = "p1")
sing_cell_data.data <- Seurat::Read10X(data.dir = req(input$sample_folder_tab1))
# Initialize the Seurat object with the raw (non-normalized data).
sing_cell_data <- Seurat::CreateSeuratObject(counts = sing_cell_data.data,
project = input$proj_name,
min.cells = input$min_cells,
min.features = input$min_features)
# Calculate the % of mithocondrial contamination
sing_cell_data <- Seurat::PercentageFeatureSet(sing_cell_data,
pattern = input$mito_regex,
col.name = "percent.mt")
return(sing_cell_data)
})
observeEvent( input$load_10X, {
output$target_genes_mitho <- renderPrint({
sc_object <- req(single_cell_data_reac())
mito_regex <- req(input$mito_regex)
assay <- DefaultAssay(object = sc_object)
mitho_genes <- grep(pattern = mito_regex,
x = rownames(x = sc_object[[assay]]),
value = TRUE)
if ( length(mitho_genes) <= 0) {
print("No gene target by the common identifier!")
}else {
mitho_genes
}
})
})
output$VlnPlot <- renderPlot({
data_set <- req( single_cell_data_reac() )
on.exit(removeNotification(id = "p1"), add = TRUE)
Seurat::VlnPlot(data_set,
features = c("nFeature_RNA", "nCount_RNA", "percent.mt"),
ncol = 3,
split.plot = F)
})
observeEvent(input$run_vinplot, {
data_sc <- req( single_cell_data_reac() )
test_cond <- if( !is.na(input$max_count) && !is.na(input$min_count) ) {
shinyFeedback::feedbackWarning("max_count",
input$min_count > input$max_count,
"No cells will be selected by appling this parameters!")
shinyFeedback::feedbackWarning("min_count",
input$min_count > input$max_count,
"No cells will be selected by appling this parameters!")
validate(need(input$min_count < input$max_count, "Error: No cells will be selected by appling this parameters!"))
}
if ( !is.na(input$min_count) ) {
data_sc <- base::subset(data_sc,
subset = nFeature_RNA > input$min_count)
}
if ( !is.na(input$max_count) ) {
shinyFeedback::feedbackWarning("max_count",
input$max_count <= 0,
"No cells will be selected by appling this parameters!")
validate(need(input$max_count > 0, "Error: No cells will be selected by appling this parameters!"))
data_sc <- base::subset(data_sc,
subset = nFeature_RNA < input$max_count)
}
if ( !is.na(input$max_mito_perc) ) {
shinyFeedback::feedbackWarning("max_mito_perc",
input$max_mito_perc <= 0,
"No cells will be selected by appling this parameters!")
validate(need(input$max_mito_perc > 0, "Error: No cells will be selected by appling this parameters!"))
data_sc <- base::subset(data_sc,
subset = percent.mt < input$max_mito_perc)
}
single_cell_data_filt <- data_sc
output$VlnPlot_filt <- renderPlot({
Seurat::VlnPlot(single_cell_data_filt,
features = c("nFeature_RNA", "nCount_RNA", "percent.mt"),
ncol = 3,
split.plot = F)
})
})
output$p1_down <- downloadHandler(
filename = function() {
paste("Violin_plot", ".", input$p1_format, sep = "")
},
content = function(file) {
withProgress(message = "Please wait, preparing the data for download.",
value = 0.5, {
data_sc <- req( single_cell_data_reac() )
test_cond <- if( !is.na(input$max_count) && !is.na(input$min_count) ) {
shinyFeedback::feedbackWarning("max_count",
input$min_count > input$max_count,
"No cells will be selected by appling this parameters!")
shinyFeedback::feedbackWarning("min_count",
input$min_count > input$max_count,
"No cells will be selected by appling this parameters!")
validate(need(input$min_count < input$max_count, "Error: No cells will be selected by appling this parameters!"))
}
if ( !is.na(input$min_count) ) {
data_sc <- base::subset(data_sc,
subset = nFeature_RNA > input$min_count)
}
if ( !is.na(input$max_count) ) {
shinyFeedback::feedbackWarning("max_count",
input$max_count <= 0,
"No cells will be selected by appling this parameters!")
validate(need(input$max_count > 0, "Error: No cells will be selected by appling this parameters!"))
data_sc <- base::subset(data_sc,
subset = nFeature_RNA < input$max_count)
}
if ( !is.na(input$max_mito_perc) ) {
shinyFeedback::feedbackWarning("max_mito_perc",
input$max_mito_perc <= 0,
"No cells will be selected by appling this parameters!")
validate(need(input$max_mito_perc > 0, "Error: No cells will be selected by appling this parameters!"))
data_sc <- base::subset(data_sc,
subset = percent.mt < input$max_mito_perc)
}
height <- as.numeric( req( input$p1_height) )
width <- as.numeric( req( input$p1_width) )
res <- as.numeric( req( input$p1_res) )
p <- Seurat::VlnPlot(data_sc,
features = c("nFeature_RNA", "nCount_RNA", "percent.mt"),
ncol = 3,
split.plot = F)
ggplot2::ggsave(file,
p,
height=height,
width=width,
units="cm",
dpi=res,
bg = "#FFFFFF")
})
}
)
single_cell_data_pca <- eventReactive( c(input$run_pca, input$rerun_after_filtering), {
sc_data <- req( single_cell_data_reac() )
if (input$filter_clusters == 1) { to_filter <- req( to_filter() )}
req(input$min_count)
req(input$max_count)
req(input$max_mito_perc)
req(input$filter_clusters)
req(input$filter_clusters_opt)
req(input$most_var_method)
if (input$normaliz_method == 0) { #LogNormalize
shinyFeedback::feedbackWarning("scale_factor", is.na(input$scale_factor), "Required value")
shinyFeedback::feedbackWarning("n_of_var_genes", is.na(input$n_of_var_genes), "Required value")
req(input$scale_factor)
req(input$n_of_var_genes)
sc_data2 <- lognorm_function("Test",
to_filter = to_filter,
sc_data = sc_data,
min_count = input$min_count,
max_count = input$max_count,
max_mito_perc = input$max_mito_perc,
scale_factor = input$scale_factor,
filter_clusters = input$filter_clusters,
filter_clusters_opt = input$filter_clusters_opt,
most_var_method = input$most_var_method,
n_of_var_genes = input$n_of_var_genes)
} else if (input$normaliz_method == 1) { #"SCTransform"
sc_data2 <- SCTransform_function("Test",
to_filter = to_filter,
sc_data = sc_data,
min_count = input$min_count,
max_count = input$max_count,
max_mito_perc = input$max_mito_perc,
filter_clusters = input$filter_clusters,
filter_clusters_opt = input$filter_clusters_opt)
# This won't be used until the DE analysis and visualization. Here is a good place to perform the normalization since it works even after the user filter the clusters of interest.
sc_data2 <- NormalizeData(sc_data2,
assay = "RNA",
normalization.method = "LogNormalize",
scale.factor = 10000)
all_genes <- rownames(sc_data2@assays$RNA)
sc_data2 <- Seurat::ScaleData(sc_data2,
assay = "RNA",
features = all_genes)
}
sc_data2
})
observeEvent(c(input$run_pca, input$rerun_after_filtering), {
output$n_of_PCAs <- renderPlot({
data_sc <- req( single_cell_data_pca() )
Seurat::ElbowPlot(data_sc, ndims = 50, reduction = "pca")
})
})
output$p2_down <- downloadHandler(
filename = function() {
paste("Elbow_Plot", ".", input$p2_format, sep = "")
},
content = function(file) {
withProgress(message = "Please wait, preparing the data for download.",
value = 0.5, {
shinyFeedback::feedbackWarning("p2_height", is.na(input$p2_height), "Required value")
shinyFeedback::feedbackWarning("p2_width", is.na(input$p2_width), "Required value")
height <- as.numeric( req( input$p2_height) )
width <- as.numeric( req( input$p2_width) )
res <- as.numeric(input$p2_res)
p <- Seurat::ElbowPlot(req( single_cell_data_pca()), ndims = 50, reduction = "pca")
ggplot2::ggsave(file,
p,
height=height,
width=width,
units="cm",
dpi=res,
bg = "#FFFFFF")
})
}
)
clusters_single_cell_data_reso_umap <- reactive({
sc_data <- req( single_cell_data_reso_umap() )
as.numeric( unique( as.character(sc_data@meta.data$seurat_clusters ) ) )
})
output$cluster_list_ui <- renderUI ({
clusters <- req( clusters_single_cell_data_reso_umap() )
shinyWidgets::pickerInput(
inputId = "cluster_list",
label = "Choose clusters to select or exclude",
choices = sort(clusters),
multiple = TRUE,
options = list(`actions-box` = TRUE)
)
})
to_filter <- reactive({
shinyFeedback::feedbackWarning("cluster_list",
is.null(input$cluster_list),
"Required value")
req(input$cluster_list)
if ( input$filter_clusters_opt == 0 ) { # "select"
to_filter <- base::subset(req( single_cell_data_reso_umap() ),
idents = as.numeric(input$cluster_list))
to_filter_ch <- to_filter@meta.data
to_filter_ch <- base::rownames(to_filter_ch)
to_filter_ch
} else if ( input$filter_clusters_opt == 1 ) { # exclude
to_filter <- base::subset(req( single_cell_data_reso_umap() ),
idents = as.numeric(input$cluster_list),
invert = TRUE)
to_filter_ch <- to_filter@meta.data
to_filter_ch <- base::rownames(to_filter_ch)
to_filter_ch
}
})
single_cell_data_reso_umap <- eventReactive( list(input$run_clustering, input$load_10X_rds), {
if ( input$sample_tab1_options == 1) { # Load file
showNotification("Loading the data",
id = "m9",
duration = NULL)
sc_data <- readRDS( paste0("./RDS_files/", req(input$select_sample_tab1_rds)) )
validate(need("seurat_clusters" %in% colnames(sc_data@meta.data),
"Clustering was not detected in the rds file", ""))
on.exit(removeNotification(id = "m9"), add = TRUE)
} else if (input$sample_tab1_options == 0 ) { # new analysis
shinyFeedback::feedbackWarning("n_of_PCs", is.na(input$n_of_PCs), "Required value")
shinyFeedback::feedbackWarning("resolution_clust", is.na(input$resolution_clust), "Required value")
req(input$n_of_PCs)
req(input$resolution_clust)
showNotification("Running the clustering step",
duration = NULL,
id = "m6")
data_sc <- req( single_cell_data_pca() )
data_sc <- Seurat::FindNeighbors(data_sc, dims = 1:input$n_of_PCs)
data_sc <- Seurat::FindClusters(data_sc, resolution = input$resolution_clust)
sc_data <- Seurat::RunUMAP(data_sc, dims = 1:input$n_of_PCs)
sc_data <- Seurat::RunTSNE(sc_data, dims = 1:input$n_of_PCs)
}
sc_data
})
observeEvent( list(input$run_clustering, input$load_10X_rds), {
output$tSNE <- renderPlot({
Seurat::DimPlot(req( single_cell_data_reso_umap() ), reduction = "tsne", label = T, pt.size = .1)
})
output$umap <- renderPlot({
Seurat::DimPlot(req( single_cell_data_reso_umap()), reduction = "umap", label = T, pt.size = .1)
})
output$cluster_size <- renderPrint({
sing_cell_data <- req( single_cell_data_reso_umap() )
sc_meta <- as.data.frame(sing_cell_data[[]])
sc_meta$cellcluster <- base::rownames(sc_meta)
sc_meta <- sc_meta[, c("cellcluster", "seurat_clusters")]
on.exit(removeNotification(id = "m6"), add = TRUE)
base::table(sc_meta$seurat_clusters)
})
})
output$p3_down <- downloadHandler(
filename = function() {
paste("clustering_plot_", input$p3_down_opt, ".", input$p3_format, sep = "")
},
content = function(file) {
withProgress(message = "Please wait, preparing the data for download.",
value = 0.5, {
shinyFeedback::feedbackWarning("p3_height", is.na(input$p3_height), "Required value")
shinyFeedback::feedbackWarning("p3_width", is.na(input$p3_width), "Required value")
height <- as.numeric( req( input$p3_height) )
width <- as.numeric( req( input$p3_width) )
res <- as.numeric(input$p3_res)
if ( input$p3_down_opt == "UMAP" ) {
p <- Seurat::DimPlot( req( single_cell_data_reso_umap() ), reduction = "umap", label = T, pt.size = .1)
} else if ( input$p3_down_opt == "t-SNE") {
p <- Seurat::DimPlot(req( single_cell_data_reso_umap() ), reduction = "tsne", label = T, pt.size = .1)
}
ggplot2::ggsave(file,
p,
height=height,
width=width,
units="cm",
dpi=res,
bg = "#FFFFFF")
})
}
)
output$downloadRDS <- downloadHandler(
filename = function() {
paste("clustered_dataset", ".rds", sep = "")
},
content = function(file) {
withProgress(message = "Please wait, preparing the data for download.",
value = 0.5, {
saveRDS( req( single_cell_data_reso_umap() ), file)
})
}
)
# We only change the assay here because it will allow the users to filter the clusters and still use the SCT assay for the PCA and clustering (when using SCTransform normalization. It has no affect if using lognormalization).
single_cell_data_reso_umap_to_DE_vis <- reactive({
sc_data <- single_cell_data_reso_umap()
DefaultAssay(sc_data) <- "RNA"
sc_data
})
output$find_markers_clust_id_tab1_ui <- renderUI ({
clusters <- req( clusters_single_cell_data_reso_umap() )
shinyWidgets::pickerInput(
inputId = "find_markers_clust_id_tab1",
label = "Select the cluster of interest",
choices = sort(clusters),
multiple = FALSE,
options = list(`actions-box` = TRUE))
})
output$find_markers_clust_ID1_tab1_ui <- renderUI ({
clusters <- req( clusters_single_cell_data_reso_umap() )
shinyWidgets::pickerInput(
inputId = "find_markers_clust_ID1_tab1",
label = "Select the cluster of interest",
choices = sort(clusters),
multiple = F,
options = list(`actions-box` = TRUE))
})
output$find_markers_clust_ID2_tab1_ui <- renderUI ({
clusters <- req( clusters_single_cell_data_reso_umap() )
# Exclude the cluster already selected in the option 1, since the two cluster must be different
clusters <- clusters[ !clusters == input$find_markers_clust_ID1_tab1]
shinyWidgets::pickerInput(
inputId = "find_markers_clust_ID2_tab1",
label = "Select the cluster(s) to compare",
choices = sort(clusters),
multiple = T,
options = list(`actions-box` = TRUE,
"max-options-group" = 1),
selected = sort(clusters)[1])
})
markers_tab1 <- eventReactive( input$run_ident_markers_tab1, {
shinyFeedback::feedbackWarning("find_markers_tab1_return_thresh",
is.na(input$find_markers_tab1_return_thresh),
"Required value")
shinyFeedback::feedbackWarning("find_markers_tab1_logfc_threshold",
is.na(input$find_markers_tab1_logfc_threshold),
"Required value")
shinyFeedback::feedbackWarning("find_markers_tab1_min_pct",
is.na(input$find_markers_tab1_min_pct),
"Required value")
req(input$find_markers_tab1_return_thresh)
req(input$find_markers_tab1_logfc_threshold)
req(input$find_markers_tab1_min_pct)
sc_data <- req( single_cell_data_reso_umap() )
if (input$find_markers_tab1_opt == 2) { #distinguishing a cluster from other
ident_1 = input$find_markers_clust_ID1_tab1
ident_2 = input$find_markers_clust_ID2_tab1
} else if (input$find_markers_tab1_opt == 1 ) {
ident_1 = input$find_markers_clust_id_tab1
}
showNotification("Identifing markers or D.E. genes",
duration = NULL,
id = "tab1_n1")
finding_markers("finding_markers_tab1",
sc_data,
assay_choice = "RNA",
find_markers_tab1_opt = input$find_markers_tab1_opt,
find_markers_tab1_return.thresh = input$find_markers_tab1_return_thresh,
find_markers_tab1_logfc.threshold = input$find_markers_tab1_logfc_threshold,
find_markers_tab1_min.pct = input$find_markers_tab1_min_pct,
find_markers_tab1_test.use = input$find_markers_tab1_test.use,
ident.1 = ident_1,
ident.2 = ident_2,
find_markers_tab1_filt_pos = input$find_markers_tab1_filt_pos
)
})
output$markers_tab1_react <- renderReactable({
markers_tab1 <- req( markers_tab1() )
if (input$find_markers_tab1_opt == 1) {
markers_tab1$cluster <- isolate(input$find_markers_clust_id_tab1)
markers_tab1 <- markers_tab1[, c( 1, ncol(markers_tab1), 2 : ( ncol(markers_tab1) - 1) ) ]
} else if (input$find_markers_tab1_opt == 2) {
markers_tab1$cluster <- paste0( isolate(input$find_markers_clust_ID1_tab1),
"_vs_" ,
isolate(input$find_markers_clust_ID2_tab1))
markers_tab1 <- markers_tab1[, c( 1, ncol(markers_tab1), 2 : ( ncol(markers_tab1) - 1) ) ]
} else if (input$find_markers_tab1_opt == 0) {
markers_tab1 <- markers_tab1[, c(1, ncol(markers_tab1), 2 : ( ncol(markers_tab1) - 1) ) ]
}
on.exit(removeNotification(id = "tab1_n1"), add = TRUE)
my_reactable(markers_tab1)
})
output$download_markers_tab1 <- downloadHandler(
filename = function() {
if ( input$find_markers_tab1_opt == 0 ) {
paste("list_of_markers_all_clusters", ".csv", sep = "")
} else if (input$find_markers_tab1_opt == 1) {
paste("list_of_markers_", "cluster", input$find_markers_clust_id_tab1, ".csv", sep = "")
} else if (input$find_markers_tab1_opt == 2) {
paste("list_of_markers_", "cluster", input$find_markers_clust_ID1_tab1, "vs" , input$find_markers_clust_ID2_tab1, ".csv", sep = "")
}
},
content = function(file) {
withProgress(message = "Please wait, preparing the data for download.",
value = 0.5, {
markers_tab1 <- req( markers_tab1() )
if (input$find_markers_tab1_opt == 1) {
markers_tab1$cluster <- input$find_markers_clust_id_tab1
markers_tab1 <- markers_tab1[, c( 1, ncol(markers_tab1), 2 : ( ncol(markers_tab1) - 1) ) ]
} else if (input$find_markers_tab1_opt == 2) {
markers_tab1$cluster <- paste0(input$find_markers_clust_ID1_tab1, "_vs_" , input$find_markers_clust_ID2_tab1)
markers_tab1 <- markers_tab1[, c( 1, ncol(markers_tab1), 2 : ( ncol(markers_tab1) - 1) ) ]
} else if (input$find_markers_tab1_opt == 0) {
markers_tab1 <- markers_tab1[, c(1, ncol(markers_tab1), 2 : ( ncol(markers_tab1) - 1) ) ]
}
write.csv(markers_tab1, file, row.names = FALSE)
})
}
)
features <- reactive({
req(input$markers_list)
ext <- tools::file_ext(input$markers_list$name)
markers_list_file <- input$markers_list$datapath
if ( input$markers_list_header_opt == "" ) {
shinyFeedback::feedbackWarning("markers_list_header_opt",
TRUE,
"Please, inform if the file has a header.")
}
req(input$markers_list_header_opt)
read_file_markers("tab1_readfile",
markers_list_file = markers_list_file,
feed_ID ="markers_list",
ext = ext,
header_opt = input$markers_list_header_opt)
})
observeEvent(input$load_markers, {
output$marker_group_selec = renderUI({
pickerInput_markers_group("features_group", genes = req( features() ) )
})
output$marker_genes_selec = renderUI({
features_group <- req(input$features_group)
id_choice <- req(input$genes_ids)
pickerInput_markers_genes("selected_genes",
genes = req( features() ),
features_group = features_group,
id_choice = id_choice)
})
})
filt_features <- eventReactive(input$run_heatmap, {
features_f <- req( features() )
features_group <- req(input$features_group)
features_f <- dplyr::filter(features_f,
Group %in% features_group)
if (input$filter_genes_q == 0) {
shinyFeedback::feedbackWarning("selected_genes",
is.null(input$selected_genes),
"Please, select one or more genes")
req(input$selected_genes)
if (input$genes_ids == "ID") {
features_f <- features_f[features_f$GeneID %in% input$selected_genes, ]
} else if (input$genes_ids == "name") {
features_f <- features_f[features_f$Name %in% input$selected_genes, ]
}
}
features_f
})
sc_data_av_react <- eventReactive(input$run_heatmap, {
sc_data_av <- Seurat::AverageExpression( req( single_cell_data_reso_umap() ),
assays = "RNA",
slot = input$slot_selection_heatmap)
sc_data_av <- sc_data_av[[1]]
on.exit(removeNotification(id = "m7"), add = TRUE)
sc_data_av
})
observeEvent(input$run_heatmap, {
# This calculate the height of the plot based on the n of genes so the plot can be adjusted to fit all genes
heatmap_n_genes <- reactive({
req(filt_features())
heatmap_genes(features = filt_features(), sc_data_av = req( sc_data_av_react()) )
})
heatmap_Height <- reactive( 150 + ( 20 * req( heatmap_n_genes() ) ) )
heat_map_prep <- reactive({
req(filt_features())
features <- filt_features()
sc_data_av <- req( sc_data_av_react() )
features_selec <- as.data.frame(unique(features$GeneID))
if (nrow(features) == 1) {
sc_data_av_feat <- sc_data_av[base::rownames(sc_data_av) %in% features_selec[, 1], ]
sc_data_av_feat <- t(sc_data_av_feat)
base::rownames(sc_data_av_feat) <- features_selec[1, 1]
} else {
sc_data_av_feat <- sc_data_av[base::rownames(sc_data_av) %in% features_selec[, 1], ]
}
sc_data_av_feat
})
output$heat_map <- renderPlot({
req(heat_map_prep())
heat_map_prep <- heat_map_prep()
ComplexHeatmap::Heatmap(heat_map_prep, border = TRUE,
rect_gp = gpar(col = "white", lwd = 2),
column_title = "Clusters",
column_title_side = "bottom",
name = "Expression",
show_row_dend = T)
}, height = heatmap_Height())
output$heat_map_ui <- renderUI({
plotOutput("heat_map", height = req( heatmap_Height() ) )
})
output$marker_to_feature_plot = renderUI({
feature_plots_gene_selection("selected_genes_for_feature_plot",
genes_names = req( filt_features() ),
sc_data_av_react = req( sc_data_av_react() ),
genes_ids = input$genes_ids)
})
output$p4_down <- downloadHandler(
filename = function() {
paste("Heatmap", ".", input$p4_format, sep = "")
},
content = function(file) {
withProgress(message = "Please wait, preparing the data for download.",
value = 0.5, {
shinyFeedback::feedbackWarning("p4_height", is.na(input$p4_height), "Required value")
shinyFeedback::feedbackWarning("p4_width", is.na(input$p4_width), "Required value")
height <- as.numeric( req( input$p4_height) )
width <- as.numeric( req( input$p4_width) )
res <- as.numeric(input$p4_res)
p <- ComplexHeatmap::Heatmap(req( heat_map_prep() ), border = TRUE,
rect_gp = gpar(col = "white", lwd = 2),
column_title = "Clusters",
column_title_side = "bottom",
name = "Expression",
show_row_dend = T)
# Complex heatmap does not work well with ggplot2::ggsave. So, we use grid.grap to make it compatible.
gb = grid.grabExpr(draw(p))
ggplot2::ggsave(file,
gb,
height=height,
width=width,
units="cm",
dpi=res,
bg = "#FFFFFF")
})
}
)
})
features_selec <- eventReactive(input$run_feature_plot, {
shinyFeedback::feedbackWarning("selected_genes_for_feature_plot",
is.null(input$selected_genes_for_feature_plot),
"Please, select one or more genes.")
req(input$selected_genes_for_feature_plot)
features <- req( filt_features() )