pathway-enrichment

To identify enriched pathways from differentially expressed genes, this Skill analyzes your gene list to detect over-represented biological pathways and gene sets. It leverages gseapy and gprofiler-official to compute enrichment and generate interpretable biological insights.

Gene set enrichment analysis identifies statistically over-represented biological pathways and gene sets within a gene list. It helps interpret differential expression data by revealing coordinated biological processes rather than analyzing genes individually.

Yes, this pathway analysis approach fully supports gseapy and gprofiler-official alongside pandas, numpy, scipy, and matplotlib. These dependencies are integrated to handle data processing, statistical computation, and visualization of enriched gene sets.

The best way to analyze gene expression data for over-represented biological processes is to input a gene list into an enrichment analysis workflow. This Skill uses gseapy and gprofiler-official to map genes to pathways and calculate statistical significance.

Yes, you need a pre-filtered gene list, typically derived from differential expression studies. The Skill takes this text file as input and applies statistical methods using scipy to identify significantly enriched pathways and gene sets.

Gene set enrichment analysis requires scipy for statistical calculations like p-value computation and multiple testing correction. Matplotlib is used to generate plots visualizing the enriched pathways, ensuring the biological interpretation results are both quantifiable and visually accessible.