dcNetwork {dcanr} | R Documentation

## Generate a differential network from a DC analysis

### Description

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

### Usage

    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

### Details

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

### Value

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

### 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)

[Package dcanr version 1.10.0 Index]