enrichLabelNets {netDx} | R Documentation

## Score networks based on their edge bias towards (+,+) interactions

### Description

Score networks based on their edge bias towards (+,+) interactions

### Usage

    enrichLabelNets(netDir, pheno_DF, outDir, numReps = 50L, minEnr = -1,
      outPref = "enrichLabelNets", verbose = TRUE, setSeed = 42L,
      enrType = "binary", numCores = 1L, predClass, tmpDir = tempdir(),
      netGrep = "_cont.txt$", getShufResults = FALSE, ...)

### Arguments

`netDir` | (char) path to dir containing all networks
`pheno_DF` | (data.frame) for details see `getEnr()`
`outDir` | (char) path to dir where output/log files are written
`numReps` | (integer) Max num reps for shuffling class status. Adaptive permutation is used so in practice, few networks would be evaluated to this extent
`minEnr` | (numeric from -1 to 1) Only include networks with ENR value greater than this threshold.
`outPref` | (char) prefix for log file (not counting the dir name)
`verbose` | (logical) print messages
`setSeed` | (integer) if not NULL, integer is set as seed to ensure reproducibility in random number generation
`enrType` | (char) see getEnr()
`numCores` | (integer) num cores for parallel ENR computation of all networks
`predClass` | (char) see `getEnr()`
`tmpDir` | (char) path to dir where temporary work can be stored
`netGrep` | (char) pattern to grep for network files in netDir
`getShufResults` | (logical) if TRUE, returns the ENR for each permutation, for all networks. Warning: this is likely to be huge. Use this flag for debugging purposes only.
`...` | parameters for `countIntType_batch()`.

### Details

Determines which networks are statistically enriched for interactions between the class of interest. The resulting `ENR` score and corresponding p-value serve as a filter to exclude random-like interaction networks before using feature selection. This filter is known to be important when patient networks are sparse and binary; e.g. networks based on shared overlap of CNV locations. If the filter is not applied, GeneMANIA WILL promote networks with slight bias towards (+,+) edges , even if these are small and random-like.

The measure of (+,+)-enrichment is defined as: ENR(network N) = ((num (+,+) edges) - (num other edges))/(num edges). A p-value for per-network ENR is obtained non-parametrically by measuring a null distribution for ENR following multiple permutations of case-control labels.

### Value

(data.frame) networks stats from clique-filtering, one record per network

### Examples

    data(npheno)
    netDir <- system.file("extdata","example_nets",package="netDx")
    x <- enrichLabelNets(netDir,npheno,".",predClass="case",netGrep="txt$",
    	numReps=5)
    print(x)

[Package netDx version 1.1.4 Index]