enrichLabelNets: Score networks based on their edge bias towards (+,+)...

In BaderLab/netDx: Network-based patient classifier

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)

BaderLab/netDx documentation built on Sept. 26, 2021, 9:13 a.m.