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25 February 2026
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09:19 | (Upload log) [Brb (4×)] | |||
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09:19 Brb talk contribs uploaded File:Wgcna null power.png | ||||
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09:19 Brb talk contribs uploaded File:Wgcna null dend.png (<syntaxhighlight lang='r'> library(WGCNA) # Set seed for reproducibility set.seed(123) # 1. Create a "Scaffold" for 3 modules (patterns of expression) nSamples = 50 # 2. Generate 1000 genes based on these patterns + some noise # Module 1: 200 genes correlated with pattern 1 noise = matrix(rnorm(nSamples*1000), nrow=nSamples) # 3. Combine into one expression matrix (Samples as Rows, Genes as Columns) datExpr2 = as.data.frame(noise) colnames(datExpr2) = paste0("Gene", 1:1000) rownames(datEx...) | ||||
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09:16 Brb talk contribs uploaded File:Wgcna 3clusters power.png | ||||
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09:16 Brb talk contribs uploaded File:Wgcna 3clusters dend.png (<syntaxhighlight lang='r'> # WGCNA uses standard hierarchical clustering (specifically the hclust function in R), # but the "magic" that makes it WGCNA is the Topological Overlap Matrix (TOM) used as the distance metric. # https://en.wikipedia.org/wiki/Weighted_correlation_network_analysis # https://cloud.wikis.utexas.edu/wiki/spaces/bioiteam/pages/47714689/Clustering+using+WGCNA # https://ngs101.com/how-to-build-gene-co-expression-networks-from-rna-seq-data-using-wgcna-complete-step-by-ste...) | ||||
