Methods and an evaluation framework for the inference of differential co-expression/association networks.
Download the package from Bioconductor
Or install the development version of the package from Github.
Load the installed package into an R session.
This example shows how a differential network can be derived. Simulated data within the package is used.
#load simulated data data(sim102) #get expression data and conditions for 'UME6' knock-down simdata <- getSimData(sim102, cond.name = 'UME6', full = FALSE) emat <- simdata$emat ume6_kd <- simdata$condition #apply the z-score method with Spearman correlations z_scores <- dcScore(emat, ume6_kd, dc.method = 'zscore', cor.method = 'spearman') #perform a statistical test: the z-test is selected automatically raw_p <- dcTest(z_scores, emat, ume6_kd) #adjust p-values (raw p-values from dcTest should NOT be modified) adj_p <- dcAdjust(raw_p, f = p.adjust, method = 'fdr') #get the differential network dcnet <- dcNetwork(z_scores, adj_p) #> Warning in dcNetwork(z_scores, adj_p): default thresholds being selected plot(dcnet, vertex.label = '', main = 'Differential co-expression network')
Edges in the differential network are coloured based on the score (negative to positive represented from purple to green respectively).