getDataByFilter {BioMM} | R Documentation

## Return the data by feature filtering

### Description

Identify and select a subset of outcome-associated or predictive features in the training data based on filtering methods. Return the same set of selected features for the test data if it is available.

### Usage

    getDataByFilter(
      trainData,
      testData,
      FSmethod,
      cutP = 0.1,
      fdr = NULL,
      FScore = MulticoreParam()
    )

### Arguments

`trainData` | The input training dataset. The first column is the label.
`testData` | The input test dataset. The first column is the label.
`FSmethod` | Feature selection methods. Available options are c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox', or 'top10pCor'). 'positive' is the positively outcome-associated features using the Pearson correlation method. 'posWilcox' is the positively outcome-associated features using Pearson correlation method together with 'wilcox.text' method. 'top10pCor' is the top 10 outcome-associcated features. This is helpful when no features can be picked during stringent feature selection procedure.
`cutP` | The cutoff used for p value thresholding. It can be any value between 0 and 1. Commonly used cutoffs are c(0.5, 0.1, 0.05, 0.01, etc.). The default is 0.1.
`fdr` | Multiple testing correction method. Available options are c(NULL, 'fdr', 'BH', 'holm' etc). See also `p.adjust`. The default is NULL.
`FScore` | The number of cores used for some feature selection methods. If it's NULL, then no parallel computing is applied.

### Details

Parallel computing is helpful if your input data is high dimensional. For 'cutP', a soft thresholding of 0.1 may be favorable than more stringent p value cutoff because the features with small effect size can be taken into consideration for downstream analysis. However, for high dimensional (e.g. p > 10,000) data, many false positive features may exist, thus, rigorous p value thresholding should be applied. The choice of feature selection method depends on the characteristics of the input data.

### Value

Both training and test data (if provided) with pre-selected features are returned if feature selection method is applied. If no feature can be selected during feature selection procedure, then the output is NULL.

### Author(s)

Junfang Chen

### Examples

    ## Load data
    methylfile <- system.file('extdata', 'methylData.rds', package='BioMM')
    methylData <- readRDS(methylfile)
    trainIndex <- sample(nrow(methylData), 20)
    trainData = methylData[trainIndex,]
    testData = methylData[-trainIndex,]
    ## Feature selection
    library(BiocParallel)
    param <- MulticoreParam(workers = 10)
    ## Select outcome-associated features based on the Wilcoxon test (P<0.1)
    datalist <- getDataByFilter(trainData, testData, FSmethod="wilcox.test",
                               cutP=0.1, fdr=NULL, FScore=param)
    trainDataSub <- datalist[[1]]
    testDataSub <- datalist[[2]]
    print(dim(trainData))
    print(dim(trainDataSub))

[Package BioMM version 1.6.0 Index]