Fast Gene Set Enrichment Analysis (fgsea) is now available on Vecura

What is fgsea?

fgsea (Fast Gene Set Enrichment Analysis) is a highly efficient Bioconductor R package designed to identify biological pathways that are significantly altered in transcriptomics data. By utilizing adaptive multilevel Monte-Carlo sampling, it achieves extremely high precision in p-value calculation, capable of resolving values as small as 1e-50. It helps users interpret complex biological data by connecting lists of genes or expression patterns to functional pathways, enabling deeper insights into underlying molecular mechanisms. It is especially useful for researchers working with RNA-seq, microarray, or single-cell datasets who require accurate FDR (False Discovery Rate) control when testing thousands of pathways simultaneously.

What can users do with fgsea on Vecura?

With fgsea on Vecura, users can:

• Perform Pre-ranked GSEA: Identify pathways enriched at the top or bottom of a ranked gene list, such as those derived from log fold changes or t-statistics.
• Run Over-representation Analysis (ORA): Statistically determine which pathways are over-represented in a specific subset of "interesting" genes, such as those that are differentially expressed.
• Execute GESECA (Gene Set Co-regulation Analysis): Analyze multi-sample expression matrices to find gene sets that co-vary across conditions, perfect for time-course or single-cell experiments without a specific control group.
• Integrate Custom Gene Sets: Easily provide pathway definitions via JSON or standard GMT files to tailor analyses to specific biological questions.

What the output means

The output provides structured results tables containing raw and BH-adjusted p-values, enrichment scores (or variance explained for GESECA), and lists of leading-edge or overlapping genes. These tables quantify the strength and statistical significance of pathway associations.

This output should be used to support scientific decision making. It does not replace experimental validation.

Why this matters

Understanding which biological pathways are active under different experimental conditions is a fundamental challenge in bioinformatics. Traditional GSEA tools can struggle with precision when analyzing large gene sets, potentially leading to inaccurate FDR control and obscured biological signals. fgsea addresses these limitations by providing a faster, more sensitive, and statistically robust alternative.

By streamlining this analysis into a guided workflow, researchers can move rapidly from raw expression data to actionable biological insights without the burden of managing complex R infrastructure or troubleshooting computational bottlenecks.

• Developed by: Alserg Lab (Alexey Sergushichev et al.)
• Source: Official GitHub Repo
• Reference: fgsea preprint (bioRxiv)