Hi, Thanks for the reply, i looked at the genes expressions data and i have an idea which result have more sense. At the same time i'm posting here data and code, if you have time to look at that, it will be awesome.

here the data: https://univr-my.sharepoint.com/:u:/g/personal/davide_pasini_univr_it/EaPXg7f2LrFLlXId8JoKSSkBKhhUwoeqTBMrjdW4MW_n6Q?e=fmKA1G[ link]1

here a trimmed version of the code i used, with the main workflow:

library(GSVA)
library(DESeq2)
library(stringr)
library(msigdbr)
library(ggplot2)
library(plyr)

#Functions
addScore <- function(matrix , list = NULL, max = Inf, min = -Inf, method = "gsva"){
  print(method)
  gs = gsva(expr = assay(matrix), list, min.sz = min, max.sz = max, method = method)
  print(head(gs))
  colData(matrix) = cbind(colData(matrix), t(gs))

  return(matrix)
}

converter <- function(x, type = "ENS"){
  require("biomaRt")
  human = useMart("ensembl", dataset = "hsapiens_gene_ensembl")
  mouse = useMart("ensembl", dataset = "mmusculus_gene_ensembl")

  switch(type,
         ENS = {
           genesV2 = getLDS(attributes = c("ensembl_gene_id"),
                            filters =  "ensembl_gene_id",
                            values = x,
                            mart = mouse,
                            attributesL = c("ensembl_gene_id"),
                            martL = human, uniqueRows = T)
         },
         SYMB = {
           genesV2 = getLDS(attributes = c("mgi_symbol"),
                            filters = "mgi_symbol",
                            values = x ,
                            mart = mouse,
                            attributesL = c("hgnc_symbol"),
                            martL = human, uniqueRows=T)
         },

         Human_ENS_SYMB = {
           genesV2 = getBM(attributes='hgnc_symbol', 
                 filters = 'ensembl_gene_id', 
                 values = x, 
                 mart = human ,uniqueRows=T)
         },
         Human_SYMB_ENS = {
           genesV2 = getBM(attributes = 'ensembl_gene_id', 
                           filters = 'hgnc_symbol', 
                           values = x, 
                           mart = human,uniqueRows=T)
         }
  )
  print(head(genesV2))
  return(genesV2)
}

#main code
counts = read.table("./merged_results_on_geneID.txt")

counts$gene_id = unlist(lapply(str_split(counts$gene_id, pattern = "\\."),'[',1))
counts = ddply(counts, "gene_id", numcolwise(sum))
rownames(counts) = counts$gene_id
counts$gene_id = NULL

colnames(counts) = lapply(lapply(stringr::str_split(colnames(counts), pattern = "_"),'[',c(1:4)),paste0,sep="_",collapse="")
coldata = as.data.frame(colnames(counts))
rownames(coldata) = colnames(count)
for (i in colnames(counts)) {
  counts[,i] = as.integer(counts[,i])

 }

dds = DESeqDataSetFromMatrix(counts, design = ~1, colData = coldata)

nrow(dds)
#keep <- rowSums(counts(RNA_table) >= 1) >= 21
keep <- rowSums(counts(dds)) > 1
table(keep)
RNA_filt <- dds[keep,]
nrow(RNA_filt)

RNA_filt2 <- DESeq(RNA_filt)

RNA_rl<- rlog(RNA_filt2)

Bailey = readRDS("Bailey_sig.Rds")

##########################
######### GSVA ###########
##########################

RNA_rl = addScore(matrix =  RNA_rl, list = Bailey, min = 5)

############################
######### ssGSEA ###########
############################

RNA_rl = addScore(matrix =  RNA_rl, list = Bailey, min = 5, method = "ssgsea")