predByBS: Bootstrap resampling prediction via supervised machine...

In BioMM: BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data

Description

Prediction via supervised machine learning using bootstrap resampling along with feature selection methods.

Usage

predByBS(
  trainData,
  testData,
  dataMode,
  repeats,
  FSmethod,
  cutP,
  fdr,
  FScore = MulticoreParam(),
  classifier,
  predMode,
  paramlist,
  innerCore = MulticoreParam()
)

Arguments

trainData	The input training dataset. The first column is the label or the output. For binary classes, 0 and 1 are used to indicate the class member.
testData	The input test dataset. The first column is the label or the output. For binary classes, 0 and 1 are used to indicate the class member.
dataMode	The input training data mode for model training. It is used only if 'testData' is present. It can be a subset of the whole training data or the entire training data. 'subTrain' is the given for subsetting and 'allTrain' for the entire training dataset.
repeats	The number of repeats used for boostrapping.
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.

Value

The predicted output for the test data.

Examples

 
## Load data  
methylfile <- system.file('extdata', 'methylData.rds', package='BioMM')  
methylData <- readRDS(methylfile)  
dataY <- methylData[,1]
## select a subset of genome-wide methylation data at random
methylSub <- data.frame(label=dataY, methylData[,c(2:2001)])  
trainIndex <- sample(nrow(methylSub), 16)
trainData = methylSub[trainIndex,]
testData = methylSub[-trainIndex,]
library(ranger) 
library(BiocParallel)
param1 <- MulticoreParam(workers = 1)
param2 <- MulticoreParam(workers = 20)
predY <- predByBS(trainData, testData, 
                  dataMode='allTrain', repeats=50,
                  FSmethod=NULL, cutP=0.1, 
                  fdr=NULL, FScore=param1, 
                  classifier='randForest',
                  predMode='classification', 
                  paramlist=list(ntree=300, nthreads=10),
                  innerCore=param2)  
testY <- testData[,1]
accuracy <- classifiACC(dataY=testY, predY=predY)
print(accuracy)  

BioMM documentation built on Nov. 8, 2020, 11:04 p.m.