reconBySupervised {BioMM} | R Documentation

## Reconstruct stage-2 data by supervised machine learning prediction

### Description

Reconstruct stage-2 data by supervised machine learning prediction.

### Usage

    reconBySupervised(
      trainDataList,
      testDataList,
      resample = "BS",
      dataMode,
      repeatA,
      repeatB,
      nfolds,
      FSmethod,
      cutP,
      fdr,
      FScore = MulticoreParam(),
      classifier,
      predMode,
      paramlist,
      innerCore = MulticoreParam(),
      outFileA = NULL,
      outFileB = NULL
    )

### Arguments

`trainDataList` | The input training data list containing ordered collections of matrices.
`testDataList` | The input test data list containing ordered collections of matrices.
`resample` | The resampling methods. Valid options are 'CV' and 'BS'. 'CV' for cross validation and 'BS' for bootstrapping resampling. The default is 'BS'.
`dataMode` | The mode of data used. 'subTrain' or 'allTrain'.
`repeatA` | The number of repeats N is used during resampling procedure. Repeated cross validation or multiple boostrapping is performed if N >=2. One can choose 10 repeats for 'CV' and 100 repeats for 'BS'.
`repeatB` | The number of repeats N is used for generating test data prediction scores.
`nfolds` | The number of folds is defined for cross validation.
`FSmethod` | Feature selection methods. Available options are c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox', or 'top10pCor').
`cutP` | The cutoff used for p value thresholding. Commonly used cutoffs are c(0.5, 0.1, 0.05, 0.01, etc). The default is 0.05.
`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 feature selection, if parallel computing needed.
`classifier` | Machine learning classifiers.
`predMode` | The prediction mode. Available options are c('probability', 'classification', 'regression').
`paramlist` | A set of model parameters defined in an R list object.
`innerCore` | The number of cores used for computation.
`outFileA` | The file name of stage-2 training data with the '.rds' file extension. If it's provided, then the result will be saved in this file. The default is NULL.
`outFileB` | The file name of stage-2 training data with the '.rds' file extension. If it's provided, then the result will be saved in this file. The default is NULL.

### Details

Stage-2 training data can be learned either using bootstrapping or cross validation resampling methods. Stage-2 test data is learned via independent test set prediction.

### Value

The predicted stage-2 training data and also stage-2 test data, if 'testDataList' provided. If outFileA and outFileB are provided, then the results will be stored in the files.

### Author(s)

Junfang Chen

### Examples

    ## Load data
    methylfile <- system.file('extdata', 'methylData.rds', package='BioMM')
    methylData <- readRDS(methylfile)
    ## Annotation file
    probeAnnoFile <- system.file('extdata', 'cpgAnno.rds', package='BioMM')
    featureAnno <- readRDS(file=probeAnnoFile)
    ## Mapping CpGs into Pathways
    featureAnno <- readRDS(system.file("extdata", "cpgAnno.rds", package="BioMM"))
    pathlistDB <- readRDS(system.file("extdata", "goDB.rds", package="BioMM"))
    head(featureAnno)
    dataList <- omics2pathlist(data=methylData, pathlistDB, featureAnno,
                               restrictUp=100, restrictDown=10, minPathSize=10)
    length(dataList)
    library(ranger)
    library(BiocParallel)
    param1 <- MulticoreParam(workers = 1)
    param2 <- MulticoreParam(workers = 20)
    ## Not Run, this will take a bit long
    ## stage2data <- reconBySupervised(trainDataList=dataList, testDataList=NULL,
    ##                             resample='CV', dataMode='allTrain',
    ##                             repeatA=50, repeatB=20, nfolds=10,
    ##                             FSmethod=NULL, cutP=0.1,
    ##                             fdr=NULL, FScore=param1,
    ##                             classifier='randForest',
    ##                             predMode='classification',
    ##                             paramlist=list(ntree=500, nthreads=20),
    ##                             innerCore=param2, outFileA=NULL, outFileB=NULL)
    ## print(dim(stage2data))
    ## print(head(stage2data[,1:5]))

[Package BioMM version 1.6.0 Index]