📊 A universal enrichment tool for interpreting omics data
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Updated
May 15, 2024 - R
📊 A universal enrichment tool for interpreting omics data
Gene Set Enrichment Analysis in Python
Single sample Gene Set Enrichment analysis (ssGSEA) and PTM Enrichment Analysis (PTM-SEA)
Gene Set Enrichment Analysis and Over Representation Analysis analysis using R
Differential abundance analysis for feature/ observation matrices from platforms such as RNA-seq
Brings bulk and pseudobulk transcriptomics to the tidyverse
MSigDB gene sets for multiple organisms in a tidy data format
Lightweight Iterative Gene set Enrichment in R
Testing, development environment for a new project of KN BIBS
Differential expression (DE); gene set Enrichment Analysis (GSEA); single cell RNAseq studies (scRNAseq)
Enrichment Networks for Pathway Enrichment Analysis
A web-based application to perform Gene Set Enrichment Analysis (GSEA) using clusterProfiler and shiny R libraries
Interpretation of RNAseq experiments through robust, efficient comparison to public databases
Molecular Signatures Database (MSigDB) in a data frame
Gene Set Clustering based on Functional annotation
Function Enrichment analysis and Network construction
GSEA plots in ggplot2
Thermodynamically Motivated Enrichment Analysis (TMEA) is a new approach to gene set enrichment analysis.
Pandas API for multiple Gene Set Enrichment Analysis implementations in Python (GSEApy, cudaGSEA, GSEA)
Flexible gene set enrichment analysis
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