Updated scRNA-seq analysis pipeline.

09-Single-cell_analysis/CP_enrich_seurat.R

+#!/usr/bin/env Rscript
+
+# ================================================================================================
+# Libraries
+# ================================================================================================
+library("yaml")
+library("stringr")
+library("org.Hs.eg.db")
+library("tidyverse")
+library("ReactomePA")
+library("DOSE")
+library("clusterProfiler")
+library("msigdbr")
+source("../libs/conf/confR.R")
+source("../libs/utils/utils.R")
+message(paste0("[", Sys.time(), "] Libraries loaded."))
+
+# ================================================================================================
+# Configuration
+# ================================================================================================
+options(bitmapType = "cairo")
+config          <- read_config(config_dirs = c("../Confs/", "./"))
+raw_data_dir    <- config$global_raw_data_dir
+output_data_dir <- paste0(config$global_data_dir, config$local_data_dir)
+input_data_dir  <- paste0(config$global_data_dir, config$local_input_data_dir)
+
+# Optional command line parameter to process only one dataset.
+args <- commandArgs(trailingOnly = TRUE)
+selected_dataset_name <- ""
+if (length(args) > 0) {
+  selected_dataset_name <- args[1]
+}
+rm(args)
+
+# Configure MSigDB
+msigdb <- msigdbr(species = "Homo sapiens") %>% dplyr::select(gs_name, entrez_gene)
+message(paste0("[", Sys.time(), "] Configuration done."))
+
+# ================================================================================================
+# Functions
+# ================================================================================================
+
+#' @title Perform a regular enrichment analysis using Fisher's over-representation.
+#'
+#' @description This functions relies on clusterProfiler and the associated packages
+#' to run the enrichment for a given ontology among DO, GO (all three subtypes), KEGG and
+#' REACTOME. It saves the results in the provided folder and return the enrichResult object
+#' for further plotting.
+#'
+#' @param genes The list of genes to analyse. The gene identifiers have to match what is
+#' expected - be it entrezgene identifiers or gene symbols.
+#' @param  background The list of all genes to be used as a background for correct
+#' estimation of the statistics. The genes identifiers also have to match what is expected.
+#' @param type The name of the ontology to use among "DO", "GOMF", "GOBP", "GOCC",
+#' "KEGG" and "REACTOME". The GO analyses relies on gene symbols, the others use entrezgene ids.
+#' Default to "DO".
+#' @param file_dir The folder in which the tables will be saved. Default to "".
+#' @param file_prefix The string that can be used as a prefix to name the file. Default to "".
+#' @param symplify A boolean indicating whether the results should be simplified (by removing
+#' redundant terms). Default to TRUE.
+#' @return The enrichResult object that contains the enriched terms.
+perform_enrichment <- function(genes,
+                               background,
+                               type        = "DO",
+                               file_dir    = "",
+                               file_prefix = "",
+                               simplify    = TRUE) {
+
+  # Default case, we just set the result to NULL.
+  enrich <- NULL
+  switch(type,
+         DO = {
+           enrich <- DOSE::enrichDO(gene          = genes,
+                                    universe      = background,
+                                    ont           = "DO",
+                                    minGSSize     = config$min_gs_size,
+                                    maxGSSize     = config$max_gs_size,
+                                    pvalueCutoff  = 1,
+                                    qvalueCutoff  = 1,
+                                    pAdjustMethod = "BH",
+                                    readable      = TRUE)
+         },
+         GOMF = {
+           enrich <- clusterProfiler::enrichGO(gene          = genes,
+                                               universe      = background,
+                                               keyType       = "SYMBOL",
+                                               OrgDb         = org.Hs.eg.db, #nolint
+                                               ont           = "MF",
+                                               minGSSize     = 10,
+                                               maxGSSize     = config$max_gs_size,
+                                               pvalueCutoff  = 1,
+                                               qvalueCutoff  = 1,
+                                               pAdjustMethod = "BH",
+                                               readable      = FALSE)
+         },
+         GOBP = {
+           enrich <- clusterProfiler::enrichGO(gene          = genes,
+                                               universe      = background,
+                                               keyType       = "SYMBOL",
+                                               OrgDb         = org.Hs.eg.db, #nolint
+                                               ont           = "BP",
+                                               minGSSize     = config$min_gs_size,
+                                               maxGSSize     = config$max_gs_size,
+                                               pvalueCutoff  = 1,
+                                               qvalueCutoff  = 1,
+                                               pAdjustMethod = "BH",
+                                               readable      = FALSE)
+         },
+         GOCC = {
+           enrich <- clusterProfiler::enrichGO(gene          = genes,
+                                               universe      = background,
+                                               keyType       = "SYMBOL",
+                                               OrgDb         = org.Hs.eg.db, #nolint
+                                               ont           = "CC",
+                                               minGSSize     = config$min_gs_size,
+                                               maxGSSize     = config$max_gs_size,
+                                               pvalueCutoff  = 1,
+                                               qvalueCutoff  = 1,
+                                               pAdjustMethod = "BH",
+                                               readable      = FALSE)
+         },
+         KEGG = {
+           enrich <- clusterProfiler::enrichKEGG(gene              = genes,
+                                                 universe          = background,
+                                                 organism          = "hsa",
+                                                 minGSSize         = config$min_gs_size,
+                                                 maxGSSize         = config$max_gs_size,
+                                                 pvalueCutoff      = 1,
+                                                 qvalueCutoff      = 1,
+                                                 pAdjustMethod     = "BH",
+                                                 use_internal_data = FALSE)
+         },
+         REACTOME = {
+           enrich <- ReactomePA::enrichPathway(gene          = genes,
+                                               universe      = background,
+                                               organism      = "human",
+                                               minGSSize     = config$min_gs_size,
+                                               maxGSSize     = config$max_gs_size,
+                                               pvalueCutoff  = 1,
+                                               qvalueCutoff  = 1,
+                                               pAdjustMethod = "BH",
+                                               readable      = TRUE)
+         }
+  )
+
+  # We simplify the enrichment results by removing the potentially redundant terms.
+  if (simplify == TRUE) {
+    enrich <- clusterProfiler::simplify(enrich,
+                                        cutoff     = 0.75,
+                                        by         = "p.adjust",
+                                        select_fun = min)
+  }
+
+  # We save the table as TSV file.
+  enrich_fn <- paste0(file_dir, file_prefix, "enrich_", type, ".tsv")
+  write.table(enrich, file = enrich_fn, sep = "\t", quote = FALSE, col.names = NA)
+
+  # We return the enrichResult object.
+  return(enrich)
+}
+
+
+# ================================================================================================
+# Main
+# ================================================================================================
+
+# We do all datasets one by one.
+for (i in seq_len(length(config$scdatasets))) {
+
+  # We get the dataset details.
+  dataset      <- config$scdatasets[[i]]
+  dataset_name <- dataset$dataset_name
+
+  # We run the quality control of the current dataset (if necessary).
+  if (selected_dataset_name == "" || selected_dataset_name == dataset_name) {
+
+    # We define the I/Os
+    raw_data_subdir    <- paste0(raw_data_dir,    dataset_name, "/data/")
+    output_data_subdir <- paste0(output_data_dir, dataset_name, "_seurat/")
+
+    # We are loading the background
+    background_fn <- paste0(raw_data_subdir, "genes.tsv")
+    background    <- read.table(background_fn,
+                                header    = FALSE,
+                                sep       = "\t",
+                                row.names = NULL,
+                                as.is     = TRUE)$V2
+    rm(background_fn)
+
+    # We map gene symbols to entrezgene ids for background.
+    background_map <- bitr(background,
+                           fromType = "SYMBOL",
+                           toType   = c("ENTREZID"),
+                           OrgDb    = org.Hs.eg.db) #nolint
+    rm(background)
+
+    # We perform the enrichment on all rankings files.
+    ranking_files <- list.files(path = output_data_subdir, pattern = "*rankings.tsv")
+
+    # We do all integration schemes one by one.
+    for (ranking_file in ranking_files) {
+
+      # We define the I/Os.
+      analysis_prefix <- strsplit(ranking_file, ".tsv")[[1]]
+      ofile_prefix    <- paste0("CP_", analysis_prefix, "_")
+      input_filename  <- paste0(output_data_subdir, ranking_file)
+
+      # We read the prepared ranking and keep only the value used to rank.
+      ranking <- read.delim(input_filename, stringsAsFactors = FALSE) %>%
+        select(Gene, adj_P_value)
+      rm(input_filename)
+      ranking_clean <- ranking
+      ranking_clean$adj_P_value     <- as.numeric(ranking_clean$adj_P_value)
+      rm(ranking)
+
+      # We map the gene symbols to the EntrezGene identifiers.
+      mapped_egenes <- bitr(ranking_clean$Gene,
+                            fromType = "SYMBOL",
+                            toType   = c("ENTREZID"),
+                            OrgDb    = org.Hs.eg.db) #nolint
+      ranking_final <- merge(x       = ranking_clean,
+                             y       = mapped_egenes,
+                             by.x    = "Gene",
+                             by.y    = "SYMBOL",
+                             all.x   = TRUE) %>%
+        mutate(EGene = ENTREZID) %>%
+        select(Gene, EGene, adj_P_value)
+      ranking_final$adj_P_value     <- as.numeric(ranking_final$adj_P_value)
+      ranking_final                 <- ranking_final %>% arrange(adj_P_value)
+      ranking_final_eg              <- ranking_final %>% filter(!is.na(EGene))
+      rm(mapped_egenes, ranking_clean)
+
+      # We then prepare the gene lists for Fisher-based enrichment. These are just gene lists based on
+      # the adjusted P values.
+      selection_sy <- ranking_final %>% filter(adj_P_value < 0.05)
+      selection_eg <- selection_sy  %>% filter(!is.na(EGene))
+      rm(ranking_final, ranking_final_eg)
+
+      # We loop over the functional sources.
+      for (k in seq_len(length(config$functional_sources))) {
+
+        # We define the current source.
+        func_source <- config$functional_sources[[k]]
+        if (is.null(func_source$cp_name) || func_source$cp_name == "") {
+          next
+        }
+
+        # We do not do the MSIGDB enrichment (was only for GSEA).
+        if (func_source$cp_name == "MSIGDB") {
+          next;
+        }
+
+        # We define the reference genes (depending on the source).
+        selection  <- NULL
+        background <- NULL
+        if (func_source$cp_gene_id_type == "entrezgene") {
+          selection  <- selection_eg$EGene
+          background <- background_map$ENTREZID
+        } else if (func_source$cp_gene_id_type == "symbol") {
+          selection  <- selection_sy$Gene
+          background <- background_map$SYMBOL
+        }
+
+        # We run the Fisher analysis.
+        fisher_adjP <- perform_enrichment(selection,
+                                          background,
+                                          type        = func_source$cp_name,
+                                          file_dir    = output_data_subdir,
+                                          file_prefix = ofile_prefix,
+                                          simplify    = FALSE)
+
+        message(paste0("[", Sys.time(), "][", analysis_prefix, "] CP enrichment (",
+                       func_source$cp_name, ") done."))
+        rm(func_source, fisher_adjP, selection, background)
+      } # End for each functional source.
+      rm(k, selection_sy, selection_eg)
+      rm(analysis_prefix, ofile_prefix)
+    } # End for each integration.
+    rm(ranking_file, ranking_files, output_data_subdir, raw_data_subdir, background_map)
+  } # End if dataset should be run.
+  rm(dataset, dataset_name)
+} # End for each dataset
+rm(i)
+
+# We log the session details for reproducibility.
+rm(config, input_data_dir, output_data_dir, msigdb)
+sessionInfo()

09-Single-cell_analysis/CP_enrich_seurat.sh

+#!/bin/bash -l
+#SBATCH -J geneder:09:cp_enrich
+#SBATCH --mail-type=all
+#SBATCH --mail-user=leon-charles.tranchevent@uni.lu
+#SBATCH -N 1
+#SBATCH --time=0-08:00:00
+#SBATCH --mem=10GB
+#SBATCH -p batch
+#SBATCH --qos=normal
+
+echo "== Starting run at $(date)"
+echo "== Job ID: ${SLURM_JOBID}"
+echo "== Node list: ${SLURM_NODELIST}"
+echo "== Submit dir. : ${SLURM_SUBMIT_DIR}"
+echo ""
+
+# Defining global parameters.
+OUTPUT_FOLDER=/home/users/ltranchevent/Data/GeneDER/Analysis/09/
+CODE_FOLDER=/home/users/ltranchevent/Projects/GeneDER/Analysis/09-Single-cell_analysis/
+REF=${1}
+
+# Loading modules.
+module load lang/R/3.6.2-foss-2019b-bare
+
+# Load configuration
+source ../libs/conf/confSH.sh
+create_variables ../Confs/datasets_config.yml
+
+# For all scRNA datasets
+nbScDatasets=${#scdatasets__dataset_name[@]}
+for (( i=0; i<$nbScDatasets; i++ ))
+do
+	datasetName=${scdatasets__dataset_name[$i]}
+	if [ "${datasetName}" == "${REF}" ]
+	then
+		# We run the functional enrichment.
+		echo "== Job $i started (${datasetName}) =="
+		Rscript --vanilla ${CODE_FOLDER}/CP_enrich_seurat.R ${datasetName} > ${OUTPUT_FOLDER}/${datasetName}_seurat/CP_enrich_log.out 2> ${OUTPUT_FOLDER}/${datasetName}_seurat/CP_enrich_log.err
+		echo "== Job $i ended (${datasetName}) =="
+	fi
+done
+
+# Moving the slurm log file to data
+mv ${CODE_FOLDER}/slurm-*out ${OUTPUT_FOLDER}/

09-Single-cell_analysis/Makefile

@@ -6,5 +6,28 @@ clean:
 	@rm -rf *~
 clean_outputs:
 	@rm -rf ${OUTPUT_FOLDER}/*
-analyse:
-	@sbatch ${CODE_FOLDER}analyse.sh
+	@/bin/bash ${CODE_FOLDER}getdata.sh
+s_ana:
+	@sbatch --time=0-02:30:00 --mem=64GB            ${CODE_FOLDER}analyse_seurat.sh GSE157783
+	@sbatch --time=0-20:00:00 --mem=196GB -p bigmem ${CODE_FOLDER}analyse_seurat.sh GSE178265
+#s_ana_stest:
+#	@sbatch --time=0-00:20:00 --mem=8GB ${CODE_FOLDER}sanalyse_seurat.sh GSE157783s
+#	@sbatch --time=0-00:20:00 --mem=8GB ${CODE_FOLDER}sanalyse_seurat.sh GSE178265s
+#s_ana_mtest:
+#	@sbatch --time=0-02:00:00 --mem=64GB ${CODE_FOLDER}analyse_seurat.sh GSE157783m
+#	@sbatch --time=0-02:00:00 --mem=64GB ${CODE_FOLDER}analyse_seurat.sh GSE178265m
+l_ana:
+	@sbatch --time=0-17:00:00 --mem=64GB            ${CODE_FOLDER}analyse_liger.sh GSE157783
+	@sbatch --time=2-00:00:00 --mem=400GB -p bigmem ${CODE_FOLDER}analyse_liger.sh GSE178265
+#l_ana_stest:
+#	@sbatch --time=0-00:20:00 --mem=6GB ${CODE_FOLDER}sanalyse_liger.sh GSE157783s
+#	@sbatch --time=0-00:20:00 --mem=6GB ${CODE_FOLDER}sanalyse_liger.sh GSE178265s
+#l_ana_mtest:
+#	@sbatch --time=0-02:00:00 --mem=64GB ${CODE_FOLDER}analyse_liger.sh GSE157783m
+#	@sbatch --time=0-02:00:00 --mem=64GB ${CODE_FOLDER}analyse_liger.sh GSE178265m
+prep:
+	@sbatch                              ${CODE_FOLDER}prepare_rankings_seurat.sh GSE157783
+	#@sbatch                              ${CODE_FOLDER}prepare_rankings_seurat.sh GSE178265
+enrich:
+	@sbatch                              ${CODE_FOLDER}CP_enrich_seurat.sh GSE157783
+	#@sbatch                              ${CODE_FOLDER}CP_enrich_seurat.sh GSE178265

09-Single-cell_analysis/README.md

@@ -2,6 +2,7 @@
 The objectives of this step is to analyse the single-cell data (PD versus control) for male and female samples.
 
 # Details and instructions
+Beware, we use a version of SEurat that needs spatstat v1.64 not v2.x.
 
 # Prerequisites
-A prerequisite is to have the scRNA data ready in the project data folder.
\ No newline at end of file
+A prerequisite is to have the scRNA data ready in the project data folder.

09-Single-cell_analysis/analyse.sh → 09-Single-cell_analysis/analyse_liger.sh

 #!/bin/bash -l
-#SBATCH -J geneder:09:scRNA
+#SBATCH -J geneder:09:liger
 #SBATCH --mail-type=all
 #SBATCH --mail-user=leon-charles.tranchevent@uni.lu
 #SBATCH -N 1
 #SBATCH --ntasks-per-socket=14
 #SBATCH --ntasks-per-node=28
 #SBATCH -c 1
-#SBATCH --mem=32GB
-#SBATCH --time=0-02:00:00
+#SBATCH --mem=64GB
+#SBATCH --time=0-04:00:00
 #SBATCH -p batch
 #SBATCH --qos=normal
 
@@ -20,6 +20,7 @@ echo ""
 # Defining global parameters.
 OUTPUT_FOLDER=/home/users/ltranchevent/Data/GeneDER/Analysis/09/
 CODE_FOLDER=/home/users/ltranchevent/Projects/GeneDER/Analysis/09-Single-cell_analysis/
+REF=${1}
 
 # Loading modules.
 module load lang/R/3.6.2-foss-2019b-bare
@@ -28,22 +29,20 @@ module load lang/R/3.6.2-foss-2019b-bare
 source ../libs/conf/confSH.sh
 create_variables ../Confs/datasets_config.yml
 
-# We get the Ensembl and HGNC data
-wget -O ${OUTPUT_FOLDER}mitochondrial_genes.tsv 'http://www.ensembl.org/biomart/martservice?query=<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE Query><Query  virtualSchemaName = "default" formatter = "TSV" header = "0" uniqueRows = "0" count = "" datasetConfigVersion = "0.6" ><Dataset name = "hsapiens_gene_ensembl" interface = "default" ><Filter name = "chromosome_name" value = "MT"/><Attribute name = "ensembl_gene_id" /></Dataset></Query>'
-wget -O ${OUTPUT_FOLDER}ribosomalrna_genes.tsv  'https://www.genenames.org/cgi-bin/genegroup/download?id=848&type=branch'
-
 # For all scRNA datasets
-#nbScDatasets=${#scdatasets__dataset_name[@]}
-#for (( i=0; i<$nbScDatasets; i++ ))
-#do
-	i=1
+nbScDatasets=${#scdatasets__dataset_name[@]}
+for (( i=0; i<$nbScDatasets; i++ ))
+do
 	datasetName=${scdatasets__dataset_name[$i]}
-	echo "== Job $i started (${datasetName}) =="
-	rm -rf ${OUTPUT_FOLDER}${datasetName}/
-	mkdir ${OUTPUT_FOLDER}${datasetName}/
-	Rscript --vanilla ${CODE_FOLDER}analyse.R ${datasetName} > ${OUTPUT_FOLDER}${datasetName}/analysis_log.out 2> ${OUTPUT_FOLDER}${datasetName}/analysis_log.err
-	echo "== Job $i ended (${datasetName}) =="
-#done
+	if [ "${datasetName}" == "${REF}" ]
+	then
+		echo "== Job $i started (${datasetName}) =="
+		rm -rf ${OUTPUT_FOLDER}${datasetName}_liger/
+		mkdir ${OUTPUT_FOLDER}${datasetName}_liger/
+		Rscript --vanilla ${CODE_FOLDER}analyse_liger.R ${datasetName} > ${OUTPUT_FOLDER}${datasetName}_liger/analysis_log.out 2> ${OUTPUT_FOLDER}${datasetName}_liger/analysis_log.err
+		echo "== Job $i ended (${datasetName}) =="
+	fi
+done
 
 # Moving the slurm log file to data
 mv ${CODE_FOLDER}/slurm-*out ${OUTPUT_FOLDER}/

09-Single-cell_analysis/analyse_seurat.sh

+#!/bin/bash -l
+#SBATCH -J geneder:09:seurat
+#SBATCH --mail-type=all
+#SBATCH --mail-user=leon-charles.tranchevent@uni.lu
+#SBATCH -N 1
+#SBATCH --ntasks-per-socket=14
+#SBATCH --ntasks-per-node=28
+#SBATCH -c 1
+#SBATCH --mem=64GB
+#SBATCH --time=0-04:00:00
+#SBATCH -p batch
+#SBATCH --qos=normal
+
+echo "== Starting run at $(date)"
+echo "== Job ID: ${SLURM_JOBID}"
+echo "== Node list: ${SLURM_NODELIST}"
+echo "== Submit dir. : ${SLURM_SUBMIT_DIR}"
+echo ""
+
+# Defining global parameters.
+OUTPUT_FOLDER=/home/users/ltranchevent/Data/GeneDER/Analysis/09/
+CODE_FOLDER=/home/users/ltranchevent/Projects/GeneDER/Analysis/09-Single-cell_analysis/
+REF=${1}
+
+# Loading modules.
+module load lang/R/3.6.2-foss-2019b-bare
+
+# Load configuration
+source ../libs/conf/confSH.sh
+create_variables ../Confs/datasets_config.yml
+
+# For all scRNA datasets
+nbScDatasets=${#scdatasets__dataset_name[@]}
+for (( i=0; i<$nbScDatasets; i++ ))
+do
+	datasetName=${scdatasets__dataset_name[$i]}
+	if [ "${datasetName}" == "${REF}" ]
+	then
+		echo "== Job $i started (${datasetName}) =="
+		rm -rf ${OUTPUT_FOLDER}${datasetName}_seurat/
+		mkdir ${OUTPUT_FOLDER}${datasetName}_seurat/
+		Rscript --vanilla ${CODE_FOLDER}analyse_seurat.R ${datasetName} > ${OUTPUT_FOLDER}${datasetName}_seurat/analysis_log.out 2> ${OUTPUT_FOLDER}${datasetName}_seurat/analysis_log.err
+		echo "== Job $i ended (${datasetName}) =="
+	fi
+done
+
+# Moving the slurm log file to data
+mv ${CODE_FOLDER}/slurm-*out ${OUTPUT_FOLDER}/

09-Single-cell_analysis/compute_gender_specificity_seurat.R

+#!/usr/bin/env Rscript
+
+# ================================================================================================
+# Libraries
+# ================================================================================================
+library("yaml")
+library("tidyverse")
+library("ggplot2")
+source("../libs/conf/confR.R")
+source("../libs/utils/utils.R")
+message(paste0("[", Sys.time(), "] Libraries loaded."))
+
+# ================================================================================================
+# Configuration
+# ================================================================================================
+options(bitmapType = "cairo")
+config          <- read_config(config_dirs = c("../Confs/", "./"))
+output_data_dir <- paste0(config$global_data_dir, config$local_data_dir)
+input_data_dir  <- paste0(config$global_data_dir, config$local_input_data_dir)
+
+# Optional command line parameter to process only one dataset.
+args <- commandArgs(trailingOnly = TRUE)
+selected_dataset_name <- ""
+if (length(args) > 0) {
+  selected_dataset_name <- args[1]
+}
+rm(args)
+
+message(paste0("[", Sys.time(), "] Configuration done."))
+
+# ================================================================================================
+# Functions
+# ================================================================================================
+
+#' @title Function to compute the gender-specificity score based on the Pi value rankings.
+#' @description This function takes a dataframe with the log fold changes and P values of two
+#' comparisons termed "ref" and "ctrl". The idea is to compute a score representing their
+#' specificity for "ref" using "ctrl" to correct the non specific DEGs.
+#' The specificity is based on the Pi values so the actual regulation (up/down) does not
+#' matter and two genes with strong but opposite signals will not be considered specific.
+#' @param FM The data-frame that contains the experimental data. The expected columns are
+#' ref_logFC, ref_Pval, ref_adj_Pval, ctrl_logFC, ctrl_Pval, and ctrl_adj_Pval.
+#' @param has_ctrl A boolean indicating whether a "ctrl" comparison is present. If not then
+#' only the Pi value computation and the associated rankings are performed for "ref", and
+#' no delta values are returned.
+#' @return An enriched data-frame that contains additional columns, in particular one that
+#' contains the gender-specificity scores, representing the specificity to the "ref" comparison
+#' (unless has_ctrl is set to FALSE).
+compute_genderspecificity_scores <- function(FM, has_ctrl = TRUE) {
+
+  # First, we compute the Pi values for both comparisons ("ref" and "control"). For this, we use
+  # the nominal P values and the log fold changes. Using the corrected P values would be more
+  # difficult since most values would be 1. We anyway intend to use the gender specific rankings
+  # produced here for GSEA, and only the ranking matters, not so much the actual values.
+  #
+  # We will add two fields:
+  #   ref_pivalue:
+  #               The Pi values for the "ref" comparison. Values are ~[0, 100].
+  #               Small values correspond to non differentially expressed genes, high values
+  #               correspond to differentially expressed genes. These values are the ones used to
+  #               create the ranking for GSEA when we analyse gender DEGs (and not gender specific
+  #               DEGs).
+  #   ctrl_pivalue:
+  #               The Pi values for the "ctrl" comparison. Values are ~[0, 100].
+  #               Small values correspond to non differentially expressed genes, high values
+  #               correspond to differentially expressed genes. These values are the ones used to
+  #               create the ranking for GSEA when we analyse gender DEGs (and not gender specific
+  #               DEGs).
+  #
+
+  # We start by the most obvious cases for which all values are present.
+  # The Pi value is simply the absolute log fold change * the P value (on a log10 scale).
+  FM$ref_pivalue    <- -log10(FM$ref_Pval)  * abs(FM$ref_logFC)
+  if (has_ctrl) {
+    FM$ctrl_pivalue <- -log10(FM$ctrl_Pval) * abs(FM$ctrl_logFC)
+  }
+
+  # We continue with special cases, that is when P value == 1.
+  # These are the genes that are definitely not differentially expressed.
+  # Because P value == 1, we have Pi value == 0.
+  # We use a small workaround to substract a small epsilon to the original P value.
+  # This way, all genes with P value == 1 are still ranked (therefore based on their log
+  # fold change only). This is because we want to avoid too many ties.
+  pseudo_pval_ref    <- 0.1 * (9 + max(FM$ref_Pval[FM$ref_Pval   < 1]))
+  pseudo_pi_ref      <- -log10(pseudo_pval_ref)  * abs(FM$ref_logFC[FM$ref_pivalue   == 0])
+  FM$ref_pivalue[FM$ref_pivalue   == 0]   <- pseudo_pi_ref
+  if (has_ctrl) {
+    pseudo_pval_ctrl <- 0.1 * (9 + max(FM$ctrl_Pval[FM$ctrl_Pval < 1]))
+    pseudo_pi_ctrl   <- -log10(pseudo_pval_ctrl) * abs(FM$ctrl_logFC[FM$ctrl_pivalue == 0])
+    FM$ctrl_pivalue[FM$ctrl_pivalue == 0] <- pseudo_pi_ctrl
+    rm(pseudo_pval_ctrl, pseudo_pi_ctrl)
+  }
+  rm(pseudo_pval_ref, pseudo_pi_ref)
+
+  # We then aim at creating a delta that represents the difference between the differential
+  # expression in "ref" and in "ctrl". The comparison is done based on the rankings, which
+  # are themseleves based on the Pi values. We can not use the Pi values directly since they