What is co-expression analysis?
Weighted gene co-expression network analysis (WGCNA) is a bioinformatics application for exploring the relationships between different gene sets (modules), or between gene sets and clinical features (Langfelder and Horvath, 2008).
What is an Eigengene?
eigengene (plural eigengenes) (genetics) (mathematics) One of a set of right singular vectors of a genes x samples matrix that tabulates, e.g., the mRNA or gene expression of the genes across the samples.
What is meant by coexpressed genes?
Genes Thought to Be Involved in the Same Biological Process Are Coexpressed. The KEGG database defines genes that are thought to function in the same biological process, such as in a metabolic or regulatory pathway.
What are Eigengenes in WGCNA?
The module eigengene E is defined as the first principal component of a given module. It can be considered a representative of the gene expression profiles in a module. Eigengene significance.
What are module Eigengenes?
Module eigengene is defined as the first principal component of the expression matrix of the corresponding module. The calculation may fail if the expression data has too many missing entries. Handling of such errors is controlled by the arguments subHubs and trapErrors .
What does WGCNA stand for?
Weighted gene co-expression network analysis
Weighted gene co-expression network analysis (WGCNA)6 is a popular systems biology method used to not only construct gene networks but also detect gene modules and identify the central players (i.e., hub genes) within modules.
What is gene significance in WGCNA?
A gene significance measure could also be defined by minus log of a p-value. The only requirement is that gene significance of 0 indicates that the gene is not significant with regard to the biological question of interest. The GS can take on either positive or negative.
How does weighted gene co-expression network analysis work?
WGCNA uses hierarchical clustering to identify gene modules and colour to indicate modules. For genes that are not assigned to any of the modules, WGCNA places them in a grey module. That is, genes in the grey module are not co-expressed.
How does weighted gene co expression network analysis work?
What is PPI enrichment value?
A small PPI enrichment p-value indicate that the nodes are not random and that the observed number of edges is significant. Note that is some cases enrichment is to be expected and that there numbers have to be interpreted with some caution.
Co-expression analysis. Co-expression analysis is a powerful tool for identifying genes involved in the same process. Because genes that participate in the same process are often expressed in similar ways across many experiments, we can use expression profiles of genes with known functions to identify other genes with related functions.
What is the best tool for co expression analysis?
The most widely used clustering package for co-expression analysis is Weighted Gene Correlation Network Analysis (WGCNA) [40]. This easy-to-use tool constructs co-expression modules using hierarchical clustering on a correlation network created from expression data [54].
How can differential co-expression analysis be used to identify modules?
Differential co-expression analysis can be used to identify modules that behave differently under different conditions. Potential disease genes can be identified using a guilt-by-association (GBA) approach that highlights genes that are co-expressed with multiple disease genes.
What is the minimum read depth required for co-expression analysis?
Minimum read depth and sample size required for co-expression analyses To create co-expression networks from RNA-seq data, a 20-sample minimum has been suggested [21, 54], and increased sample sizes produce networks with a higher functional connectivity [21, 59].