Description Usage Arguments Details Value Examples
View source: R/enrichLabelNets.R
Score networks based on their edge bias towards (+,+) interactions
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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,
...
)
|
netDir |
(char) path to dir containing all networks |
pheno_DF |
(data.frame) for details see |
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 |
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 |
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.
(data.frame) networks stats from clique-filtering, one record per network
1 2 3 4 5 | data(npheno)
netDir <- system.file("extdata","example_nets",package="netDx")
x <- enrichLabelNets(netDir,npheno,".",predClass="case",netGrep="txt$",
numReps=5)
print(x)
|
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