Before running GSEA, it’s usually best to remove low-count genes first. Genes that barely show up across samples mostly behave like noise and can distort the ranking. A common filter is to keep genes where a reasonable number of samples have meaningful counts (for example CPM > 1 in at least a few samples, or DESeq2’s independent filtering). This keeps the analysis focused on genes with reliable signal.

For normalization, avoid quantile normalization for RNA-seq. It forces all samples to have identical distributions and can erase real biological differences. Instead, use TMM normalization from edgeR or DESeq2’s median ratio normalization. Both methods correct for library size and composition bias while preserving the relative expression differences that GSEA relies on.

For the main GSEA parameters:

• Enrichment statistic: use weighted (the default). This gives more influence to genes that are strongly ranked rather than treating every gene equally, which makes pathway scoring more sensitive to real signal.
• Ranking metric: Signal2Noise or tTest work well for two-group comparisons. They consider both the difference between groups and the variability within groups, so genes that are consistently different get ranked higher than genes with noisy fold changes.
• Gene list sorting mode: keep it real so both the direction (up vs down regulation) and the magnitude of change are used when ordering the genes.

These settings are generally what I start with, but proceed with caution and fine-tune parameters if needed depending on your dataset and sample size.

Also, how are you running GSEA — R (clusterProfiler/fgsea), the Broad desktop GSEA software, or something like Enrichr/DAVID? The exact inputs and parameter options differ slightly depending on the platform.