Generate a differential network from a DC analysis

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

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

Arguments

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

Value

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

Details

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

See also

dcScore, dcTest, dcAdjust

Examples

#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)