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A GitHub repository for single sample Gene Set Enrichment Analysis (ssGSEA) and PTM Signature Enrichment Analysis (PTM-SEA) of gene expression and phosphoproteomics data. It includes the updated R-implementation of ssGSEA, the PTM signatures database (PTMsigDB) and the resources for gene sets and PTM signatures.
Learn how to use ssGSEA, a variation of GSEA, to calculate enrichment scores for each sample and gene set pair in CKG. See how to visualize the results with PCA on two interactomics projects from Coscia et al 2018.
ssGSEA2 is a method for single sample gene set enrichment analysis (ssGSEA) and phosphorylation site-specific signature analysis (PTM-SEA) using R. It provides an R package implementation with examples, gene set databases, and citation information.
GSVA is a method for pathway-centric analysis of molecular data by performing gene set enrichment on single samples. Learn how to use the GSVA package with RNA-seq and microarray expression data and different gene set collections.
ssGSEA is a method to project gene expression data onto a space of gene set enrichment scores for each sample. Learn how to use the ssGSEA module in GenePattern, a web-based platform for bioinformatics analysis, with parameters, references, and examples.
Each ssGSEA enrichment score represents the degree to which the genes in a particular gene set are coordinately up- or down-regulated within a sample. The ssGSEA module for GenePattern is a free genomic analysis program written in the R language implementing this method in a form suitable for GenePattern.
ssGSEAProjection projects each sample within a data set onto a space of gene set enrichment scores using the ssGSEA methodology. It transforms the data to a higher-level space representing biological processes and pathways, which can be used for analysis and interpretation.
ssGSEA Taskforce is a desktop software that allows users to run and analyze ssGSEA, a method for assessing the enrichment of gene sets in single samples. Learn how to download, install, run and interpret ssGSEA results with this tutorial.
The ssGSEA algorithm was based on 29 immune gene sets, including genes related to different immune cell types, functions, pathways, and checkpoints. We employed the ssGSEA algorithm via R packages (GSVA, GSEABase, and limma) to comprehensively assess the immunologic characteristics of every sample included in the study. 15
Single Sample Geneset Enrichment Analysis The ssGSEA method is an extension of the GSEA method, working at the level of a single sample rather than a sample population as in the original GSEA application. The score derived from ssGSEA reflects the degree to which the input gene signature is coordinately up- or downregulated within a sample.