I am doing DGE over RNASeq data of two types of cancer.

Here is the code:

keep <- filterByExpr(RNA_data, design = design)
RNA_data <- RNA_data[keep,]
RNA_data <- DGEList(counts = RNA_data, genes = rownames(RNA_data))
# Normalize the counts using the TMM method
RNA_data <- calcNormFactors(RNA_data, method = "TMM")
# Create the contrast matrix
cont.matrix <- makeContrasts(disease_leimyo-disease_lipo,
                         disease_nos-disease_leimyo,
                         disease_nos-disease_lipo, levels=design)

Voom <- voom(RNA_data, design, plot = FALSE,normalize.method = "quantile")

 vfit <- lmFit(Voom, design)
 vfit  <- contrasts.fit(vfit,cont.matrix)
 efit <- eBayes(vfit)
 deg <- topTable(efit, coef = 1,adjust.method = 'fdr', number=Inf)
 gene_list = deg$logFC
 names(gene_list) = deg$genes
 gene_list = sort(gene_list, decreasing = TRUE)
 head(gene_list)
 GO_file = "FILES/c5.go.bp.v2023.1.Hs.symbols.gmt"
 res = GSEA(gene_list, GO_file, pval = 0.05)

So as you can see I choose the coef 1 to compare only leiomyo with lipo type and want to analyse the GSEA over these two. I get this error:

> res = GSEA(gene_list, GO_file, pval = 0.05)
Error in build_Anno(TERM2GENE, TERM2NAME) : 
argument "TERM2GENE" is missing, with no default

My matrix of RNA_Data has rows gene names such as TP53,MDM2... and colnames the patient IDS. Want can I do to solve this error? Additionally, is this the best way to do GSEA after limma voom? Is the go_file the right one, I want to analyze the highest possible number of pathways.

We generally use camera to undertake GSEA analyses after limma-voom analyses, see http://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html.