dcNetwork: Generate a differential network from a DC analysis
In dcanr: Differential co-expression/association network analysis

Description Usage Arguments Details Value See Also Examples

Threshold the results from a differential co-expression analysis and create a differential network.

1	dcNetwork(dcscores, dcpvals = NULL, thresh = NULL, ...)

`dcscores`	a matrix, the result of the `dcScore` function. The results should be passed as produced by the function and not modified in intermediate steps
`dcpvals`	a matrix or NULL, raw or adjusted p-values resulting from `dcTest` or `dcAdjust` respectively. Should be left NULL only if method is EBcoexpress or DiffCoEx
`thresh`	a numeric, threshold to apply. If `NULL`, defaults to 0.1 for methods that generate a p-value, 0.9 for posterior probabilities from EBcoexpress and 0.1 on the absolute score from DiffCoEx
`...`	see details

No extra arguments required for this function. The ellipsis are used to allow flexibility in pipelines.

an igraph object, representing the differential network. Scores are added as edge attributes with the name 'score'

dcScore, dcTest, dcAdjust

#create data
set.seed(360)
x <- matrix(rnorm(120), 4, 30)
cond <- rep(1:2, 15)

#perform analysis - z-score
zscores <- dcScore(x, cond)
pvals <- dcTest(zscores, emat = x, condition = cond)
pvals <- dcAdjust(pvals, p.adjust, method = 'fdr')
ig <- dcNetwork(zscores, pvals, 0.1)

#perform analysis - DiffCoEx
dcscores <- dcScore(x, cond, dc.method = 'diffcoex')
ig <- dcNetwork(dcscores, thresh = 0.001)

#plot the resulting differential co-expression network
igraph::plot.igraph(ig)