runFeatureSelection {netDx} | R Documentation

## Run GeneMANIA cross-validation with a provided subset of networks

### Description

Run GeneMANIA cross-validation with a provided subset of networks

### Usage

    runFeatureSelection(trainID_pred, outDir, dbPath, numTrainSamps = NULL,
      incNets = "all", orgName = "predictor", fileSfx = "CV",
      verbose = FALSE, numCores = 2L, JavaMemory = 6L,
      verbose_runQuery = FALSE, debugMode = FALSE, ...)

### Arguments

`trainID_pred` | (char) vector with universe of predictor class patients (ie all that can possibly be included in the query file
`outDir` | (char) directory to store query file and GM results
`dbPath` | (char) path to GeneMANIA generic database with training population
`numTrainSamps` | (integer) number of training samples in total leave blank to use 5 training samples in order to save memory
`incNets` | (char) vector of networks to include in this analysis (features/pathway names). Useful for subset-based feature selection
`orgName` | (char) organism name for GeneMANIA generic database. The default value will likely never need to be changed.
`fileSfx` | (char) file suffix
`verbose` | (logical) print messages
`numCores` | (logical) num parallel threads for cross-validation
`JavaMemory` | (integer) memory for GeneMANIA run, in Gb.
`verbose_runQuery` | (logical) print messages for runQuery()
`debugMode` | (logical) when TRUE runs jobs in serial instead of parallel and prints verbose messages. Also prints system Java calls and prints all standard out and error output associated with these calls.
`...` | args for `makeQueries()`

### Details

Creates query files, runs GM for 10-fold cross validation.

### Value

No value. Side effect of generating feature scores.

### Examples

    data(MB.pheno)
    dbPath <- system.file("extdata","dbPath",package="netDx")
    runFeatureSelection(MB.pheno$ID[which(MB.pheno$STATUS%in% 'WNT')],
    		tempdir(),dbPath,103L)

[Package netDx version 1.0.3 Index]