Enrichr, Prerank, GSEA or ssGSEA?

Bioinformatician’s main tool for discovery has often been differential expression analysis. But between Enrichr, Prerank, GSEA, and ssGSEA, which tool should you use? Here is the quick reminder X-plainer. 🧬

The Decision Tree

The key question is: what shape is your data?

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Enrichr — you have a gene list, nothing else

Enrichr is when you just have a list of genes (can be small). No values, no conditions — just names. 📋

GENEA, GENEB, GENEC

Under the hood, Enrichr tests each gene set in its databases using a Fisher’s exact test (hypergeometric). It asks: is my list enriched for genes in pathway X more than expected by chance?

Best for: DE gene lists, hit lists from CRISPR screens, manually curated sets.

👉 Enrichr

Python: gseapy.enrichr

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Prerank — you have a ranked gene list

Prerank is when you have a continuous value per gene that you can rank — a fold change, a correlation, a t-statistic, anything. 📊

Gene	Value
GENEA	12
GENEB	8
GENEC	4

Prerank runs GSEA logic (the enrichment score / walking statistic) on your pre-ranked list, without needing raw expression data or phenotype labels. Useful when you already have a score but not the underlying samples.

Best for: correlation with a phenotype, output from another model, single-sample pseudo-bulk scores.

👉 Python: gseapy.prerank

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GSEA — you have expression data with two conditions

GSEA works best when you have a matrix of gene expression values across multiple samples with a clear phenotype label (treated vs control, disease vs healthy, etc.). It computes its own gene ranking internally. 🔬

Gene	C1	C2	C3	D1	D2	D3
GENEA	12	7	3	1	1	0
GENEB	8	0	6	8	1	1
GENEC	4	4	3	2	3	4

The key advantage over Enrichr: GSEA doesn’t require you to define a hard cutoff (“top 200 DE genes”). It uses the full ranked list and identifies pathways enriched at the top or bottom. This makes it more sensitive and less arbitrary. ✅

Best for: bulk RNA-seq, any two-condition comparison with replicates (n ≥ 3 per group recommended).

👉 GSEA software

Python: gseapy.gsea

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ssGSEA — you want a per-sample enrichment score

ssGSEA is for when you have many samples with no clear two-group contrast — or when you want a continuous enrichment score per sample rather than a comparison between groups. 🗂️

Gene	A	B	C	D	E	F
GENEA	12	7	3	1	1	0
GENEB	8	0	6	8	1	1
GENEC	4	4	3	2	3	4

Each sample gets its own enrichment score for each pathway, independently. The output is a sample × pathway matrix. Great for downstream analysis — clustering, survival analysis, correlating pathway activity with other variables.

Best for: large cohorts (TCGA, GTEx), single-cell pseudo-bulk, any analysis where you want pathway activity as a continuous feature.

👉 Python: gseapy.ssgsea

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Quick summary table

Tool	Input	Statistics	Best for
Enrichr	Gene list only	Fisher / hypergeometric	Small lists, no values
Prerank	Genes + score	GSEA walking statistic	Pre-computed rankings
GSEA	Expression matrix + 2 conditions	GSEA walking statistic	Bulk RNA-seq DE
ssGSEA	Expression matrix, no labels	Per-sample enrichment	Large cohorts, per-sample scores

For most single-cell work: compute pseudo-bulk, run GSEA or Prerank per cell type. For single-cell pathway scoring directly, decoupleR is worth a look. 🔍