diff --git a/workflow/rules/scAmpi_basic_rules.smk b/workflow/rules/scAmpi_basic_rules.smk
index 1a189bc..007ecbe 100755
--- a/workflow/rules/scAmpi_basic_rules.smk
+++ b/workflow/rules/scAmpi_basic_rules.smk
@@ -279,7 +279,7 @@ rule sctransform_preprocessing:
     conda:
         "../envs/sctransform_preprocessing.yaml"
     resources:
-        mem_mb=lambda wc, input: min(8000 + 20 * input.size_mb, max_mem_mb),
+        mem_mb=config["computingResources"]["mem_mb"]["medium"],
         runtime=config["computingResources"]["runtime"]["medium"],
     threads: config["computingResources"]["threads"]["medium"]
     log:
@@ -480,11 +480,11 @@ rule celltype_gsva:
     input:
         "results/atypical_removed/{sample}.atypical_removed.RDS"
     output:
-        outfile="results/celltype_gsva_c6/{sample}_celltype_GSVA.tsv"
+        outfile="results/celltype_gsva/{gene_set}/{sample}_celltype_GSVA.tsv"
     params:
         outdir=lambda w, output: dirname(output.outfile),
         sampleName="{sample}",
-        genesets=config["resources"]["genesets_c6"],
+        genesets=lambda w: config["tools"]["celltype_gsva"]["gene_set"][w.gene_set],
         tumor_celltypes=config["tools"]["celltype_gsva"]["celltype_set"],
         min_set_size=config["tools"]["celltype_gsva"]["min_set_size"],
         method="gsva",
@@ -496,11 +496,13 @@ rule celltype_gsva:
         runtime=config["computingResources"]["runtime"]["medium"],
     threads: config["computingResources"]["threads"]["medium"],
     log:
-        "logs/celltype_gsva_c6/{sample}.log",
+        "logs/celltype_gsva_c6/{sample}_{gene_set}.log",
     benchmark:
-        "logs/benchmark/celltype_gsva_c6/{sample}.benchmark"
+        "logs/benchmark/celltype_gsva_c6/{sample}_{gene_set}.benchmark"
     script:
         "../scripts/compute_gsva_scores_per_celltype.R"
+
+
 # generate plots about sample composition and gene expression
 rule plotting:
     input:
diff --git a/workflow/scripts/2024-01_compute_gsva_scores_per_celltype.R b/workflow/scripts/2024-01_compute_gsva_scores_per_celltype.R
new file mode 100644
index 0000000..fd32d22
--- /dev/null
+++ b/workflow/scripts/2024-01_compute_gsva_scores_per_celltype.R
@@ -0,0 +1,132 @@
+################################################################################
+## Run GSVA
+################################################################################
+# Run GSVA analysis on an SCE object in RDS file.
+# Script does not generate any plots.
+# Anne Bertolini
+# Jan 2024
+
+library(optparse)
+library(reshape2)
+library(GSVA)
+library(limma)
+library(SingleCellExperiment)
+library(stringr)
+
+options(stringsAsFactors = FALSE)
+
+# command line arguments are parsed
+option_list <- list(
+  make_option("--SCE", type = "character", help = "Path to SCE object file with input data."),
+  make_option("--subset_celltype", type = "character", help = "Celltype(s) that will be included in the analysis. Several cell types can be specified, comma separated."),
+  make_option("--outdir", type = "character", help = "Path to the directory where output files will be written."),
+  make_option("--geneset", type = "character", help = "Geneset library gmt file."),
+  make_option("--sample", type = "character", help = "Sample identifier. Attached to each output name."),
+  make_option("--min_set_size", type = "integer", help = "Minimum gene set size for it to be considered."),
+  make_option("--method", type = "character", help = "Method to calculate GSVA score. See GSVA package documentation.")
+)
+opt_parser <- OptionParser(option_list = option_list)
+opt <- parse_args(opt_parser)
+
+
+# give out date, time, session info, and input files
+cat("\n\n")
+print(Sys.time())
+cat("\n\n\nPrint sessionInfo:\n\n")
+print(sessionInfo())
+cat("\n\n\n")
+cat("\nInput files:\n\n")
+print(opt)
+cat("\n\n")
+
+
+## load input data
+my_sce <- readRDS(opt$SCE)
+print(my_sce)
+
+## identify tumour cells only
+keep_celltypes <- unlist(stringr::str_split(opt$subset_celltype, pattern = ","))
+cat("\n\n\n### Only processing cells of type:\n\n")
+print(keep_celltypes)
+cat("\n\n\n")
+
+# subset SCE object to contain only tumour cells
+mask_celltype <- colData(my_sce)$celltype_major %in% keep_celltypes
+cat("### Cells included in analysis:\n")
+print(table(mask_celltype))
+my_sce <- my_sce[, mask_celltype]
+
+# report number of cells
+if (as.integer(dim(my_sce)[2]) == 0) {
+  cat("\n### Sample", opt$sample, ": Zero cells\n\n")
+} else {
+  cat("\n### Sample", opt$sample, ":",  dim(my_sce)[2], "cells\n\n")
+}
+
+################################################################################
+
+# read gmt file with gene sets
+# make sure all columns are captured when reading in the gene set lists in gmt format
+# readLines results in a character vector with one string per gene set
+
+tmp <- readLines(opt$geneset)
+# turn tmp into list of character vectors
+tmp <- lapply(tmp, function(x) strsplit(x, "\\\t")[[1]])
+names(tmp) <- lapply(tmp, function(x) x[1])
+# remove gene set names from gene list
+gset <- lapply(tmp, function(x) x[-1])
+# remove gene set description from gene list
+gset <- lapply(gset, function(x) x[-1])
+cat("\n### Gene sets analysed:\n\n")
+names(gset)
+cat("\n\n\n")
+
+# check how many genes of the gene sets are found in the matrix
+gset_symbols <- unique(unlist(gset))
+
+# check overlap and differences
+setdiff_sce_gset <- setdiff(rownames(my_sce), gset_symbols)
+setdiff_gset_sce <- setdiff(gset_symbols, rownames(my_sce))
+stopifnot(length(intersect(setdiff_sce_gset, setdiff_gset_sce)) == 0)
+
+
+cat("\n\n### Number of genes in count matrix but not in gene sets:", length(setdiff(rownames(my_sce), gset_symbols)), "\n\n")
+cat("\n\n### Number of genes in gene sets but not in count matrix:", length(setdiff(gset_symbols, rownames(my_sce))), "\n\n")
+print(setdiff(gset_symbols, rownames(my_sce)))
+
+# have list of gene sets, HGNC symbol strings replaced with index of respective gene in SCE object
+idxs <- limma::ids2indices(gset, rownames(my_sce))
+# get unique numeric vector of all indices, occurring in gset and my_sce
+all_genes <- unique(as.numeric(unlist(idxs)))
+cat("\n### Number of unique genes in all gene sets:", length(gset_symbols), "\n\n")
+cat("\n### Number of genes found in matrix:", length(all_genes), "\n\n")
+
+# it was decided to not reduce the matrix to the sum of genes of all gene sets and keep all genes
+#t.m <- assay(my_sce, "normcounts")[all_genes, ]
+
+count_matrix <- assay(my_sce, "normcounts")
+cat("\n### Number of genes in gene sets with non-zero expression in matrix:", table(rowSums(count_matrix) == 0)[1], "\n\n")
+cat("\n### Number of genes in gene sets with zero expression in all cells:", table(rowSums(count_matrix) == 0)[2], "\n\n")
+
+# remove genes that have zero counts in all cells
+mask_genes_all_zero <- rowSums(count_matrix) != 0
+count_matrix <- count_matrix[mask_genes_all_zero, ]
+
+# estimate geneset-sample matrix from gene-sample matrix
+# GSVA
+gsva_result <- gsva(count_matrix,
+                    gset,
+                    method = opt$method,
+                    min.sz = opt$min_set_size)
+
+# reformat results
+gsva_long <- melt(gsva_result)
+names(gsva_long) <- c("gene_set", "barcode", "value")
+print("str(gsva_long):")
+print(head(gsva_long))
+
+cat("\n### Number of gene sets with results available for sample ", opt$sample, ":", length(unique(gsva_long$gene_set)), "\n\n")
+
+# write results into file
+filename <- paste0(opt$outdir,'/', opt$sample, "_celltype_GSVA.tsv")
+write.table(gsva_long, file = filename, sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
diff --git a/workflow/snakefile_seacells.smk b/workflow/snakefile_seacells.smk
index a009d97..afa0fe4 100644
--- a/workflow/snakefile_seacells.smk
+++ b/workflow/snakefile_seacells.smk
@@ -40,6 +40,9 @@ localrules:
     scAmpi_seacells,
 
 
+# gene sets for celltype GSVA
+GENE_SETS=set(config["tools"]["celltype_gsva"]["gene_set"].keys()) 
+
 # final rule of pipeline
 # defines output of scampi seacells
 rule scAmpi_seacells:
@@ -54,9 +57,6 @@ rule scAmpi_seacells:
             "results/plotting/{sample}_seacells.first_celltype.png",
             sample=sample_ids,
         ),
-        
-        
-        
         expand("results/gsva/{sample}.gsetscore_hm.png", sample=sample_ids),
         #expand("results/diff_exp_analysis/{sample}/", sample=sample_ids),
         expand(
@@ -65,6 +65,5 @@ rule scAmpi_seacells:
         ),
         expand("results/gsva/{sample}_seacells.gsetscore_hm.png", sample=sample_ids),
         "results/metacells/seacells_celltype_purity.png",
-        expand("results/celltype_gsva_c6/{sample}_seacells_celltype_GSVA.tsv", sample=sample_ids),
-        expand("results/celltype_gsva_c6/{sample}_celltype_GSVA.tsv", sample=sample_ids),
+        expand("results/celltype_gsva/{gene_set}/{sample}_seacells_celltype_GSVA.tsv", sample=sample_ids, gene_set=GENE_SETS),