predByFS: Prediction by supervised machine learning along with feature...

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

Prediction by supervised machine learning along with feature selection

Description

Prediction by supervised machine learning along with feature selection.

Usage

predByFS(
  trainData,
  testData,
  FSmethod,
  cutP,
  fdr,
  FScore = MulticoreParam(),
  classifier,
  predMode,
  paramlist
)

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.
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, etc.). The default is 0.05. If FSmethod = "posTopCor", cutP is defined as the number of most correlated features with 'fdr' = NULL.
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.
classifier	Machine learning classifiers. Available options are c('randForest', 'SVM', 'glmnet').
predMode	The prediction mode. Available options are c('probability', 'classification', 'regression').
paramlist	A set of model parameters defined in an R list object.

Details

If no feature selected or just one selected feature, then top 10

Value

The predicted output for the test data.

Author(s)

Junfang Chen

See Also

getDataByFilter

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(2:501)])  
trainIndex <- sample(nrow(methylSub), 16)
trainData = methylSub[trainIndex,]
testData = methylSub[-trainIndex,]
library(ranger) 
library(BiocParallel)
param <- MulticoreParam(workers = 10)
predY <- predByFS(trainData, testData, 
                  FSmethod='cor.test', cutP=0.1, 
                  fdr=NULL, FScore=param, 
                  classifier='randForest',
                  predMode='classification', 
                  paramlist=list(ntree=300, nthreads=20))  
testY <- testData[,1]
accuracy <- classifiACC(dataY=testY, predY=predY)
print(accuracy)  

transbioZI/BioMM documentation built on Jan. 12, 2023, 2:18 p.m.