Description Usage Arguments Details References Examples
This function reduces an expression matrix to the module level.
1 | eigengenes(dat, geneSets)
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dat |
An expression matrix or matrix-like object, with rows corresponding to
probes and columns to samples. Data is presumed to be filtered and normalized
prior to dimensionality reduction. For count data, this means undergoing some
sort of variance stabilizing transformation, such as |
geneSets |
A named list of one or several gene sets. |
Eigengenes are a convenient way to summarize data from a given gene set into a single vector of length equal to the study sample size. The eigengene of a module represents the first principle component of the sample by probe expression matrix for all genes of which the module is composed. This is useful for correlating gene sets with one another, or associating them with clinical variables of interest. They may even be used to create a meta-network of module eigengenes.
Zhang, B. & Horvath, S. (2005). "A General Framework for Weighted Gene Co-Expression Network Analysis". Stat. Appl. Genet. Molec. Biol., 4: 1, 17. https://www.ncbi.nlm.nih.gov/pubmed/16646834
Langfelder, P. & Horvath, S. (2007). "Eigengene networks for studying the relationships between co-expression modules." BMC Bioinformatics, 1: 54. http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-1-54
Horvath S. & Dong, J. (2008). "Geometric Interpretation of Gene Coexpression Network Analysis." PLoS Comput. Biol., 4(8): e1000117. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000117
1 2 3 4 5 6 7 8 9 10 11 12 | # Simulate data
mat <- matrix(rnorm(5000 * 10), nrow = 5000, ncol = 10)
grp <- rep(c("A", "B"), each = 5)
geneSets = list()
for (i in 0:10) {
genes <- ((30 * i) + 1):(30 * (i + 1))
mat[genes, grp == "A"] <- mat[genes, grp == "A"] + rnorm(1)
geneSets[[paste("Set", i)]] <- genes
}
# Create eigengene matrix
eg_mat <- eigengenes(mat, geneSets)
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