How do you analyze GSEA results?
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.
What is NES in GSEA analysis?
GSEA enrichment scores and statistics ES (enrichment score): reflects the degree to which a gene-set is overrepresented at the top or bottom of a ranked list of genes. NES (normalized enrichment score): NES corrects for differences in ES between gene-sets due to differences in gene-set sizes.
What is GSEA enrichment score?
The GSEA enrichment score (S) is the maximum value of the sum at any point in the list. Although not shown, the running sum may deviate in the negative direction, hence, S is actually the largest absolute value of the running sum. GSEA considers candidate gene sets one at a time.
How do you cite GSEA?
To cite GSEA, please reference Subramanian, Tamayo, et al. 2005 Proc Natl Acad Sci U S A 102(43):15545-50. To cite your use of the Molecular Signatures Database (MSigDB), please reference Liberzon et al. 2011 Bioinformatics 27(12):1739-40 and also the source for the gene set as listed on the gene set page.
What is a permutation in GSEA?
The GSEA test is run on each of the permuted data sets. The permutation based p-value is the number of permutation based test statistics above (or below) the value of the test statistic for the original data, divided by the number of permuted data sets.
What is the difference between go and GSEA?
Fundamentally, GSEA is an analysis method and the Gene Ontology is a dataset. There are two different types of entities present in GO: i) genes (or other macromolecules – transcripts, proteins etc); and ii) GO terms. They probably have a more formal name for the datatypes but I don’t know it.
What does high enrichment score mean?
Higher the ES score, it more likely for a gene set to shift towards either end of the ranked list L. ES is a standard Kolmogorov Smirnov statistic, where p(a tuning parameter) = 0 means the fit is good and p = 1 means the fit is not good. Normalized Enrichment Score lies [0,1].
What does negative enrichment score mean GSEA?
a negative NES will indicate that the genes in the set S will be mostly at the bottom of your list L.
What to look for in a pre ranked GSEA?
If your pre-ranked GSEA returns no significant gene sets, you may still get an idea of what roles the up- and down-regulated genes may be involved in by examining the leading edge set. This set indicates the genes that contributed to the enrichment score. The example ranks in the fgsea package were ranked on the moderated t-statistic.
What is the goal of the GSEA analysis?
The goal of GSEA is to determine whether members of a gene set S tend to occur toward the top (or bottom) of the list L, in which case the gene set is correlated with the phenotypic class distinction. The analysis can be illustrated with a figure. (We’ll get to how it was made later in the post.)
When to use gseapreranked as a ranking tool?
For example, it can be used when the ranking metric choices provided by the GSEA module are not appropriate for the data, or when a ranked list of genomic features deviates from traditional microarray expression data (e.g., GWAS results, ChIP-Seq, RNA-Seq, etc.). The user provides GSEAPreranked with a pre-ranked gene list.
What does GSEA do to the gene list?
What GSEA does is that it goes through the gene list from the top to bottom, whenever it encounters a gene that belongs to a gene set, it adds a positive score to that gene set, and if not, it penalizes the score. That is what you see in the green line in the results.