How do you analyze GSEA data?
The basic steps for running an analysis in GSEA are as follows:
- Prepare your data files: ▪ Expression dataset file (res, gct, pcl, or txt) ▪ Phenotype labels file (cls)
- Load your data files into GSEA. See Loading Data.
- Set the analysis parameters and run the analysis. See Running Analyses.
- View the analysis results.
How do you reference GSEA?
Citing GSEA To cite your use of the GSEA software, a joint project of UC San Diego and Broad Institute, please reference Subramanian, Tamayo, et al. (2005, PNAS) and Mootha, Lindgren, et al. (2003, Nature Genetics).
What is differential gene expression analysis?
Differential expression analysis means taking the normalised read count data and performing statistical analysis to discover quantitative changes in expression levels between experimental groups.
Can you use TPM for GSEA?
Note: ssGSEA (single-sample GSEA) projections perform substantially different mathematical operations from standard GSEA. For the ssGSEA implementation, gene-level summed TPM serves as an appropriate metric for analysis of RNA-seq quantifications.
How are GSEA scores calculated?
A running sum is calculated by starting at the top of the ranked list and considering each gene in succession: Add to the sum if the gene is present in gene set (red; +) and decrement the sum otherwise (-). The GSEA enrichment score (S) is the maximum value of the sum at any point in the list.
What is differential gene expression RNA-seq?
Abstract. Differential gene expression (DGE) analysis is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes across two or more conditions and is widely used in many applications of RNA-seq data analysis.
How is differential gene expression calculated?
The count data used for differential expression analysis represents the number of sequence reads that originated from a particular gene. The higher the number of counts, the more reads associated with that gene, and the assumption that there was a higher level of expression of that gene in the sample.
What is NES in GSEA?
NES = enrichment score normalized to mean enrichment of random samples of the same size. Meaning, the enrichment score given normalized based on the number of genes in the gene set, ‘same size’ is the key descriptor in the explanation.
What is FPKM expression?
FPKM stands for Fragments Per Kilobase of transcript per Million mapped reads. In RNA-Seq, the relative expression of a transcript is proportional to the number of cDNA fragments that originate from it.