# What is a leading edge gene?

Apr 13, 2020

## What is a leading edge gene?

The genes that comprise a leading edge subset have a high correlation between their expression level and the phenotype in question and tend to be at the extremes of the distribution, rather than randomly distributed.

What is GSEA used for?

Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). The Gene Set Enrichment Analysis PNAS paper fully describes the algorithm.

How are genes ranked in GSEA?

GSEA first ranks the genes based on a measure of each gene’s differential expression with respect to the two phenotypes (for example, tumor versus normal) or correlation with a continuous phenotype. Then the entire ranked list is used to assess how the genes of each gene set are distributed across the ranked list.

### 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 Normalised enrichment score?

Normalized enrichment scores (NES) indicate the distribution of Gene Ontology categories across a list of genes ranked by hypergeometrical score (HGS).

What is a ranked list of genes?

In the context of absolute gene expression, genes are ranked by mean tissue, cell type, or cell line signal. In relative gene expression biosets, the mean signal of a gene is compared to a “reference,” defined as the median signal among all tissues, cell types, or cell lines.

## How is enrichment score calculated?

Step 1: Calculation of an Enrichment Score. The score is calculated by walking down the list L, increasing a running-sum statistic when we encounter a gene in S and decreasing it when we encounter genes not in S. The magnitude of the increment depends on the correlation of the gene with the phenotype.

How does gene set enrichment work?

Gene set enrichment analysis uses a priori gene sets that have been grouped together by their involvement in the same biological pathway, or by proximal location on a chromosome. A database of these predefined sets can be found at the Molecular signatures database (MSigDB).

Why is the mootha mitochondria gene set important?

Our results associate this gene set with clinically important variation in human metabolism and illustrate the value of pathway relationships in the analysis of genomic profiling experiments.

### How many gene sets are there in GSEA?

The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets. MeSH terms Cell Line, Tumor

How is gene set enrichment analysis ( GSEA ) used?

Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.

How is GSEA used in lung cancer research?

Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.