R/BioMM.R
In transbioZI/BioMMex: BioMM: Biological-informed Multi-stage Machine learning framework
for phenotype prediction using omics data
Defines functions
BioMM
reconByUnsupervised
BioMMstage2pred
reconBySupervised
predByCV
predByBS
predByFS
baseModel
baseGLMnet
baseSVM
baseRandForest
Documented in
baseGLMnet
baseModel
baseRandForest
baseSVM
BioMM
BioMMstage2pred
predByBS
predByCV
predByFS
reconBySupervised
reconByUnsupervised
# File : BioMM.R Author: Junfang Chen
###############################################################################
#' Prediction by random forest
#'
#' @description
#' Prediction by random forest with different settings: 'probability',
#' 'classification' and 'regression'.
#' @param 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.
#' @param 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.
#' @param predMode The prediction mode. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' The valid option: list(ntree, nthreads). 'ntree' is the number of trees
#' used. The defaul is 2000. 'nthreads' is the number of threads used for
#' computation. The default is 20.
#' @return The predicted output for the test data.
#' @import ranger
#' @export
#' @author Junfang Chen
#' @examples
#' ## Load data
#' methylfile <- system.file('extdata', 'methylData.rds', package='BioMM')
#' methylData <- readRDS(methylfile)
#' dataY <- methylData[,1]
#' ## test a subset of genome-wide methylation data at random
#' methylSub <- data.frame(label=dataY, methylData[,c(2:2001)])
#' trainIndex <- sample(nrow(methylSub), 12)
#' trainData = methylSub[trainIndex,]
#' testData = methylSub[-trainIndex,]
#' library(ranger)
#' predY <- baseRandForest(trainData, testData,
#' predMode='classification',
#' paramlist=list(ntree=300, nthreads=20))
#' testY <- testData[,1]
#' accuracy <- classifiACC(dataY=testY, predY=predY)
#' print(accuracy)
baseRandForest <- function(trainData, testData, predMode = c("classification",
"probability", "regression"), paramlist = list(ntree = 2000, nthreads = 20)) {
predMode <- match.arg(predMode)
## input parameters
if (!is.element("ntree", names(paramlist))) {
stop(" 'ntree' is missing in the 'paramlist'!")
}
if (!is.element("nthreads", names(paramlist))) {
stop(" 'nthreads' is missing in the 'paramlist'!")
}
ntree <- paramlist$ntree
nthreads <- paramlist$nthreads
if (predMode == "probability") {
trainData$label <- as.factor(trainData$label)
model <- ranger(label ~ ., data = trainData, num.trees = ntree,
num.threads = nthreads, write.forest = TRUE, probability = TRUE)
yhatClassProb <- predictions(predict(model, testData))
predTest <- round(yhatClassProb[, 2], 3)
} else if (predMode == "classification") {
trainData$label <- as.factor(trainData$label)
model <- ranger(label ~ ., data = trainData, num.trees = ntree,
num.threads = nthreads, write.forest = TRUE, classification = TRUE)
predTest <- predictions(predict(model, testData))
predTest <- as.numeric(predTest) - 1
} else if (predMode == "regression") {
model <- ranger(label ~ ., data = trainData, num.trees = ntree,
num.threads = nthreads)
predTest <- predictions(predict(model, testData))
predTest <- round(predTest, 3)
}
return(predTest)
}
###############################################################################
#' Prediction by SVM
#'
#' @description
#' Prediction by support vector machine (SVM) with two different settings:
#' 'classification' and 'regression'.
#' @param 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.
#' @param 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.
#' @param predMode The prediction mode. Available options are
#' c('classification', 'probability', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' The valid option: list(kernel, gamma, cost, tuneP).
#' \enumerate{
#' \item 'tuneP': a logical value indicating if hyperparameter tuning
#' should be conducted or not. The default is FALSE.
#' \item 'kernel': options are c('linear', 'polynomial', 'radial',
#' 'sigmoid'). The defaut is 'radial'.
#' \item 'gamma': the parameter needed for all kernels except 'linear'.
#' If tuneP is TRUE, more than one value is suggested.
#' \item 'cost': is the cost of constraints violation.
#' If tuneP is TRUE, more than one value is suggested.
#' }
#' @return The predicted output for the test data.
#' @details Hyperparameter tuning is recommended in many biological data
#' mining applications. The best parameters can be determined via an internal
#' cross validation.
#' @import e1071
#' @export
#' @author Junfang Chen
##' @seealso \code{\link[e1071]{svm}}
#' @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), 12)
#' trainData = methylSub[trainIndex,]
#' testData = methylSub[-trainIndex,]
#' library(e1071)
#' predY <- baseSVM(trainData, testData,
#' predMode='classification',
#' paramlist=list(tuneP=FALSE, kernel='radial',
#' gamma=10^(-3:-1), cost=10^(-3:1)))
#' testY <- testData[,1]
#' accuracy <- classifiACC(dataY=testY, predY=predY)
#' print(accuracy)
baseSVM <- function(trainData, testData,
predMode = c("classification", "probability", "regression"),
paramlist = list(tuneP = TRUE, kernel = "radial", gamma = 10^(-3:-1),
cost = 10^(-2:2))) {
predMode <- match.arg(predMode)
## input parameters
if (!is.element("tuneP", names(paramlist))) {
stop(" 'tuneP' is missing in the 'paramlist'!")
}
if (!is.element("kernel", names(paramlist))) {
stop(" 'kernel' is missing in the 'paramlist'!")
}
if (!is.element("gamma", names(paramlist))) {
stop(" 'gamma' is missing in the 'paramlist'!")
}
if (!is.element("cost", names(paramlist))) {
stop(" 'cost' is missing in the 'paramlist'!")
}
tuneP <- paramlist$tuneP
kernel <- paramlist$kernel
gamma <- paramlist$gamma
cost <- paramlist$cost
if (tuneP == TRUE) {
kernel <- paramlist$kernel
}
if (tuneP == TRUE) {
gamma <- paramlist$gamma
}
if (tuneP == TRUE) {
cost <- paramlist$cost
}
if (predMode == "classification" || predMode == "probability") {
type <- "C-classification" ##
trainData$label <- as.factor(trainData$label)
} else if (predMode == "regression") {
type <- "eps-regression"
if (is.factor(trainData$label)) {
trainData$label <- as.numeric(trainData$label) - 1
}
}
if (predMode == "probability") {
if (tuneP == TRUE) {
## use internal CV
tuneOut <- tune.svm(label ~ ., data = trainData, type, kernel,
gamma, cost, probability = TRUE)
model <- tuneOut$best.model
} else {
## no internal CV required
model <- svm(label ~ ., data = trainData, probability = TRUE)
}
predTest2 <- predict(model, testData, probability = TRUE)
predTest <- round(attr(predTest2, "probabilities")[, 2], 3)
} else {
if (tuneP == TRUE) {
## use internal CV
tuneOut <- tune.svm(label ~ ., data = trainData, type, kernel,
gamma, cost)
model <- tuneOut$best.model
} else {
## no CV required
model <- svm(label ~ ., data = trainData)
}
predTest <- predict(model, testData)
if (predMode == "classification") {
predTest <- as.numeric(predTest) - 1
}
}
return(predTest)
}
###############################################################################
#' Prediction by generalized linear regression models
#'
#' @description
#' Prediction by generalized regression models with lasso or elastic net
#' regularization.
#' @param trainData The input training dataset. The first column is named
#' the 'label'.
#' @param testData The input test dataset. The first column is named
#' the 'label'.
#' @param predMode The prediction mode. Available options are
#' c('classification', 'probability', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' The valid option: list(family, alpha, typeMeasure, typePred).
#' \enumerate{
#' \item 'family': Response type: 'gaussian','binomial','poisson',
#' 'multinomial','cox','mgaussian'. (Default: 'binomial')
#' \item 'alpha': The elastic net mixing parameter, with 0<=alpha<= 1.
#' \item 'typeMeasure': error metrics for internal cross-validation.
#' 'mse' uses squared loss;
#' 'deviance' uses actual deviance;
#' 'mae' uses mean absolute error;
#' 'class' gives misclassification error;
#' 'auc' (for two-class logistic regression ONLY)
#' gives area under the ROC curve.
#' \item 'typePred': The type of prediction: 'response' and 'class'.
#' (Default: 'class' for binary classification)
#' }
#' @return The predicted output for the test data.
#' @export
#' @import glmnet
#' @author Junfang Chen
#' @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(glmnet)
#' ## classification
#' predY <- baseGLMnet(trainData, testData,
#' predMode='classification',
#' paramlist=list(family='binomial', alpha=0.5,
#' typeMeasure='mse', typePred='class'))
#' testY <- testData[,1]
#' accuracy <- classifiACC(dataY=testY, predY=predY)
#' print(accuracy)
baseGLMnet <- function(trainData, testData,
predMode = c("classification", "probability", "regression"),
paramlist = list(family = "binomial", alpha = 0.5, typeMeasure = "mse",
typePred = "class")) {
predMode <- match.arg(predMode)
## input parameters
if (!is.element("family", names(paramlist))) {
stop(" 'family' is missing in the 'paramlist'!")
}
if (!is.element("alpha", names(paramlist))) {
stop(" 'alpha' is missing in the 'paramlist'!")
}
if (!is.element("typeMeasure", names(paramlist))) {
stop(" 'typeMeasure' is missing in the 'paramlist'!")
}
if (!is.element("typePred", names(paramlist))) {
stop(" 'typePred' is missing in the 'paramlist'!")
}
family <- paramlist$family
alpha <- paramlist$alpha
typeMeasure <- paramlist$typeMeasure
typePred <- paramlist$typePred
trainDataY <- trainData$label
## To avoid 'X should be a matrix with 2 or more cols'
if (ncol(trainData) == 2) {
trainData$one <- rep(1, nrow(trainData))
testData$one <- rep(1, nrow(testData))
trainDataX <- trainData[, -1]
testDataX <- testData[, -1]
} else {
trainDataX <- trainData[, -1]
testDataX <- testData[, -1]
}
cvfit <- cv.glmnet(as.matrix(trainDataX), trainDataY, family = family,
type.measure = typeMeasure, nfolds = 10)
model <- glmnet(as.matrix(trainDataX), trainDataY, family = family,
alpha = alpha, lambda = cvfit$lambda.min)
yhat <- predict(model, as.matrix(testDataX), type = typePred)
if (predMode == "classification") {
## set type='class'
predTest = as.numeric(yhat)
} else if (predMode == "probability" || predMode == "regression") {
predTest = round(yhat[, 1], 3)
}
return(predTest)
}
###############################################################################
#' Base supervised machine learning models for prediction
#'
#' @description
#' Prediction using different supervised machine learning models.
#' @param 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.
#' @param 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.
#' @param classifier Machine learning classifiers.
#' Available options are c('randForest', 'SVM', 'glmnet').
#' @param predMode The prediction mode. Available options are
#' c('classification', 'probability', 'regression').
#' 'probability' is currently only for 'randForest'.
#' @param paramlist A set of model parameters defined in an R list object.
#' See more details for each individual model.
#' @return Based on a given machine learning, the predicted score/output will be
#' estimated for the test data.
#' @export
#' @author Junfang Chen
#' @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)
#' set.seed(123)
#' predY <- baseModel(trainData, testData,
#' classifier='randForest',
#' predMode='classification',
#' paramlist=list(ntree=300, nthreads=20))
#' print(table(predY))
#' testY <- testData[,1]
#' accuracy <- classifiACC(dataY=testY, predY=predY)
#' print(accuracy)
baseModel <- function(trainData, testData,
classifier = c("randForest", "SVM", "glmnet"),
predMode = c("classification", "probability", "regression"), paramlist) {
if (colnames(trainData)[1] != "label") {
stop("The first column of the 'trainData' must be the 'label'!")
}
if (colnames(testData)[1] != "label") {
stop("The first column of the 'testData' must be the 'label'!")
}
classifier <- match.arg(classifier)
predMode <- match.arg(predMode)
if (classifier == "randForest") {
predTest <- baseRandForest(trainData, testData, predMode, paramlist)
} else if (classifier == "SVM") {
predTest <- baseSVM(trainData, testData, predMode, paramlist)
} else if (classifier == "glmnet") {
predTest <- baseGLMnet(trainData, testData, predMode, paramlist)
}
return(predTest)
}
###############################################################################
#' Prediction by supervised machine learning along with feature selection
#'
#' @description
#' Prediction by supervised machine learning along with feature selection.
#' @param 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.
#' @param 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.
#' @param FSmethod Feature selection methods. Available options are
#' c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox',
#' or 'top10pCor').
#' @param 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.
#' @param fdr Multiple testing correction method. Available options are
#' c(NULL, 'fdr', 'BH', 'holm', etc.).
#' See also \code{\link[stats]{p.adjust}}. The default is NULL.
#' @param FScore The number of cores used for feature selection.
#' @param classifier Machine learning classifiers.
#' Available options are c('randForest', 'SVM', 'glmnet').
#' @param predMode The prediction mode. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' @return The predicted output for the test data.
#' @details If no feature selected or just one selected feature,
#' then top 10% outcome-associated features will be selected by default.
#' @export
#' @author Junfang Chen
#' @seealso \code{\link{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)
predByFS <- function(trainData, testData, FSmethod, cutP, fdr,
FScore = MulticoreParam(), classifier, predMode, paramlist) {
datalist <- getDataByFilter(trainData, testData, FSmethod, cutP, fdr, FScore)
## include the label
trainDataSub <- datalist[[1]]
testDataSub <- datalist[[2]]
## If no selected features or just one selected feature.
if (is.null(trainDataSub) || ncol(trainDataSub) == 2) {
datalist <- getDataByFilter(trainData, testData, FSmethod = "top10pCor",
cutP, fdr, FScore)
trainDataSub <- datalist[[1]]
testDataSub <- datalist[[2]]
predTest <- baseModel(trainData = trainDataSub, testData = testDataSub,
classifier, predMode, paramlist)
} else if (!is.null(trainDataSub)) {
predTest <- baseModel(trainData = trainDataSub, testData = testDataSub,
classifier, predMode, paramlist)
} else {
message("Error: Input data is wrong!")
}
if (is.factor(predTest)) {
predTest <- as.numeric(predTest) - 1
}
return(predTest)
}
###############################################################################
#' Bootstrap resampling prediction via supervised machine learning with
#' feature selection
#'
#' @description
#' Prediction via supervised machine learning using bootstrap resampling
#' along with feature selection methods.
#' @param 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.
#' @param 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.
#' @param 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.
#' @param repeats The number of repeats used for boostrapping.
#' @param FSmethod Feature selection methods. Available options are
#' c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox',
#' or 'top10pCor').
#' @param 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.
#' @param fdr Multiple testing correction method. Available options are
#' c(NULL, 'fdr', 'BH', 'holm', etc).
#' See also \code{\link[stats]{p.adjust}}. The default is NULL.
#' If FSmethod = "posTopCor", cutP is defined as the number of most
#' correlated features with 'fdr' = NULL.
#' @param FScore The number of cores used for feature selection
#' if parallel computing needed.
#' @param classifier Machine learning classifiers.
#' @param predMode The prediction mode. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' @param innerCore The number of cores used for computation.
#' @return The predicted output for the test data.
#' @export
#' @import BiocParallel
#' @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)
predByBS <- function(trainData, testData, dataMode, repeats, FSmethod, cutP,
fdr, FScore = MulticoreParam(), classifier, predMode, paramlist,
innerCore = MulticoreParam()) {
if (is.null(testData)) {
## BS only applied for training data
data <- trainData
predTmp <- rep(NA, nrow(data))
replist <- seq_len(repeats)
predTmpList <- bplapply(replist, function(reps) {
trainIndex <- unique(sample(nrow(data), replace = TRUE))
testIndex <- setdiff(seq_len(nrow(data)), trainIndex)
trainData <- data[trainIndex, ]
testData <- data[testIndex, ]
## OOB predicted scores
predTmp[testIndex] <- predByFS(trainData, testData, FSmethod, cutP,
fdr, FScore, classifier, predMode, paramlist)
predTmp
}, BPPARAM = innerCore)
predTest <- round(rowMeans(do.call(cbind, predTmpList), na.rm = TRUE),
3)
naCount <- sum(is.na(predTest))
## If not enough bootstrapping repeats
if (naCount > 0) {
message(paste0("Warning: ", naCount, " NA(s) in predicted testY!"))
}
} else if (!is.null(testData)) {
## repeated prediction on test data
if (dataMode == "subTrain") {
data <- trainData
predTmp <- rep(NA, nrow(data))
replist <- seq_len(repeats)
predTmpList <- bplapply(replist, function(reps) {
trainIndex <- unique(sample(nrow(data), replace = TRUE))
trainData <- data[trainIndex, ]
predTmp <- predByFS(trainData, testData, FSmethod, cutP, fdr,
FScore, classifier, predMode, paramlist)
predTmp
}, BPPARAM = innerCore)
predTest <- round(rowMeans(do.call(cbind, predTmpList)), 3)
} else if (dataMode == "allTrain") {
predTmp <- rep(NA, nrow(testData))
replist <- seq_len(repeats)
predTmpList <- bplapply(replist, function(reps) {
predTmp <- predByFS(trainData, testData, FSmethod, cutP, fdr,
FScore, classifier, predMode, paramlist)
predTmp
}, BPPARAM = innerCore)
predTest <- round(rowMeans(do.call(cbind, predTmpList)), 3)
}
} else {
message("Input data is wrong!")
}
if (predMode == "classification") {
predTest <- ifelse(predTest >= 0.5, 1, 0)
}
return(predTest)
}
###############################################################################
#' Cross validation prediction by supervised machine learning and feature
#' selection
#' @description
#' Prediction by supervised machine learning models using cross validation
#' along with feature selection methods.
#' @param data The input dataset. The first column
#' is the label or the output. For binary classes,
#' 0 and 1 are used to indicate the class member.
#' @param repeats The number of repeats used for cross validation.
#' Repeated cross validation is performed if N >=2.
#' @param nfolds The number of folds is defined for cross validation.
#' @param FSmethod Feature selection methods. Available options are
#' c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox',
#' or 'top10pCor').
#' @param 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.
#' If FSmethod = "posTopCor", cutP is defined as the number of most
#' correlated features with 'fdr' = NULL.
#' @param fdr Multiple testing correction method. Available options are
#' c(NULL, 'fdr', 'BH', 'holm', etc).
#' See also \code{\link[stats]{p.adjust}}. The default is NULL.
#' @param FScore The number of cores used for feature selection if parallel
#' computing needed.
#' @param classifier Machine learning classifiers.
#' @param predMode The prediction mode. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' @param innerCore The number of cores used for computation.
#' @return The predicted cross validation output.
#' @export
#' @import BiocParallel
#' @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)])
#' library(ranger)
#' library(BiocParallel)
#' param1 <- MulticoreParam(workers = 1)
#' param2 <- MulticoreParam(workers = 20)
#' predY <- predByCV(methylSub, repeats=1, nfolds=10,
#' FSmethod=NULL, cutP=0.1,
#' fdr=NULL, FScore=param1,
#' classifier='randForest',
#' predMode='classification',
#' paramlist=list(ntree=300, nthreads=1),
#' innerCore=param2)
#' dataY <- methylData[,1]
#' accuracy <- classifiACC(dataY=dataY, predY=predY)
#' print(accuracy)
predByCV <- function(data, repeats, nfolds, FSmethod, cutP, fdr, FScore = MulticoreParam(),
classifier, predMode, paramlist, innerCore = MulticoreParam()) {
cvMat <- c()
replist <- seq_len(repeats)
for (reps in replist) {
foldlists <- split(sample(nrow(data)), rep(seq_len(nfolds),
length = nrow(data)))
cvY <- rep(NA, nrow(data))
cvEstimate <- bplapply(foldlists, function(fold) {
trainData <- data[-fold, ]
testData <- data[fold, ]
predTest <- predByFS(trainData, testData, FSmethod, cutP, fdr,
FScore, classifier, predMode, paramlist)
}, BPPARAM = innerCore)
cvY[unlist(foldlists)] <- unlist(cvEstimate)
cvMat <- cbind(cvMat, cvY)
}
if (repeats == 1) {
predTest <- cvMat[, 1]
} else {
## average over the repeats
predTest <- round(rowMeans(cvMat, na.rm = TRUE), 3)
}
if (predMode == "classification") {
predTest <- ifelse(predTest >= 0.5, 1, 0)
}
return(predTest)
}
###############################################################################
#' Reconstruct stage-2 data by supervised machine learning prediction
#' @description
#' Reconstruct stage-2 data by supervised machine learning prediction.
#' @param trainDataList The input training data list containing ordered
#' collections of matrices.
#' @param testDataList The input test data list containing ordered collections
#' of matrices.
#' @param resample The resampling methods. Valid options are 'CV' and 'BS'.
#' 'CV' for cross validation and 'BS' for bootstrapping resampling.
#' The default is 'BS'.
#' @param dataMode The mode of data used. 'subTrain' or 'allTrain'.
#' @param 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'.
#' @param repeatB The number of repeats N is used for generating test data
#' prediction scores.
#' @param nfolds The number of folds is defined for cross validation.
#' @param FSmethod Feature selection methods. Available options are
#' c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox',
#' or 'top10pCor').
#' @param 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.
#' If FSmethod = "posTopCor", cutP is defined as the number of most
#' correlated features with 'fdr' = NULL.
#' @param fdr Multiple testing correction method. Available options are
#' c(NULL, 'fdr', 'BH', 'holm', etc).
#' See also \code{\link[stats]{p.adjust}}. The default is NULL.
#' @param FScore The number of cores used for feature selection, if parallel
#' computing needed.
#' @param classifier Machine learning classifiers.
#' @param predMode The prediction mode. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' @param innerCore The number of cores used for computation.
#' @param 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.
#' @param 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.
#' @return 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.
#' @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.
#' @export
#' @import stats
#' @import BiocParallel
#' @author 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]))
reconBySupervised <- function(trainDataList, testDataList, resample = "BS",
dataMode, repeatA, repeatB, nfolds, FSmethod, cutP, fdr, FScore = MulticoreParam(), classifier,
predMode, paramlist, innerCore = MulticoreParam(), outFileA = NULL, outFileB = NULL) {
reconDataAx <- c()
reconDataBx <- c()
for (i in seq_along(trainDataList)) {
## for each block
trainData = trainDataList[[i]]
dataAy <- trainData[, 1]
message(paste0('Block:', i))
message(paste0('Block_numFeature: ', c(ncol(trainData)-1)))
if (resample == "CV") {
predA <- predByCV(data = trainData, repeats = repeatA, nfolds,
FSmethod, cutP, fdr, FScore, classifier, predMode, paramlist,
innerCore)
} else if (resample == "BS") {
predA <- predByBS(trainData, testData = NULL, dataMode,
repeats = repeatA, FSmethod, cutP, fdr, FScore, classifier,
predMode, paramlist, innerCore)
}
reconDataAx <- cbind(reconDataAx, predA)
if (!is.null(testDataList)) {
## ind. prediction
testData = testDataList[[i]]
dataBy <- testData[, 1]
predB <- predByBS(trainData, testData, dataMode, repeats = repeatB,
FSmethod, cutP, fdr, FScore, classifier, predMode, paramlist,
innerCore)
reconDataBx <- cbind(reconDataBx, predB)
}
}
row.names(reconDataAx) <- row.names(trainData) ##
reconDataA <- data.frame(label = dataAy, reconDataAx)
colnames(reconDataA) <- c("label", names(trainDataList))
## Provide fileName
if (!is.null(outFileA)) {
saveRDS(reconDataA, file = outFileA)
}
if (!is.null(testDataList)) {
## ind. prediction
row.names(reconDataBx) <- row.names(testData) ##
reconDataB <- data.frame(label = dataBy, reconDataBx)
colnames(reconDataB) <- c("label", names(testDataList))
if (!is.null(outFileB)) {
saveRDS(reconDataB, file = outFileB)
}
result <- list(reconDataA, reconDataB)
} else {
result <- reconDataA
}
return(result)
}
###############################################################################
#' Prediction performance for stage-2 data using supervised machine learning
#' @description
#' Prediction performance for reconstructed stage-2 data using supervised
#' machine learning with feature selection methods.
#' @param trainData The input training dataset (stage-2 data). The first column
#' is the label or the output. For binary classes,
#' 0 and 1 are used to indicate the class member.
#' @param testData The input test dataset (stage-2 data). The first column
#' is the label or the output. For binary classes,
#' 0 and 1 are used to indicate the class member.
#' @param dataMode The mode of data used. 'subTrain' or 'allTrain'.
#' @param repeatA The number of repeats N is used during resampling prediction.
#' The default is 1.
#' @param repeatB The number of repeats N is used for test data prediction.
#' The default is 1.
#' @param nfolds The number of folds is defined for cross validation.
#' @param FSmethod Feature selection methods. Available options are
#' c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox',
#' 'top10pCor', 'posTopCor').
#' @param 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.
#' If FSmethod = "posTopCor", cutP is then defined as the number of most
#' correlated features with 'fdr' = NULL.
#' @param fdr Multiple testing correction method. Available options are
#' c(NULL, 'fdr', 'BH', 'holm', etc).
#' See also \code{\link[stats]{p.adjust}}. The default is NULL.
#' @param FScore The number of cores used for feature selection if parallel
#' computing needed.
#' @param classifier Machine learning classifiers.
#' @param predMode The prediction mode. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' @param innerCore The number of cores used for computation.
#' @return The CV or BS predicted score for stage-2 training data and
#' test set predicted score for stage-2 test data if the test set is
#' given.
#' @details Stage-2 prediction is performed typically using positively
#' correlated features. Since negative associations likely reflect random
#' effects in the underlying data
#' @export
#' @import stats
#' @import utils
#' @references Perlich, C., & Swirszcz, G. (2011). On cross-validation and
#' stacking: Building seemingly predictive models on random data. ACM SIGKDD
#' Explorations Newsletter, 12(2), 11-15.
#' @author Junfang Chen
BioMMstage2pred <- function(trainData, testData, dataMode,
repeatA = 1, repeatB = 1, nfolds, FSmethod, cutP, fdr,
FScore = MulticoreParam(), classifier,
predMode, paramlist, innerCore = MulticoreParam() ) {
if (is.factor(testData$label)) {
testData$label <- as.numeric(testData$label) - 1
}
testY <- testData$label
predY <- predByBS(trainData, testData, dataMode, repeats = repeatB,
FSmethod, cutP, fdr, FScore, classifier, predMode, paramlist,
innerCore)
result <- predY
return(result)
}
###############################################################################
#' Reconstruct stage-2 data by PCA
#' @description
#' Stage-2 data reconstruction by regular or sparse constrained principal
#' component analysis (PCA).
#' @param trainDataList The input training data list containing ordered
#' collections of matrices.
#' @param testDataList The input test data list containing ordered collections
#' of matrices.
#' @param typeMode The type of PCA prediction mode. Available options are
#' c('regular', 'sparse').
#' (Default: regular)
#' @param topPC The number of top PCs selected. The default is 1,
#' i.e. the first PC.
#' @param innerCore The number of cores used for computation.
#' @param 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.
#' @param 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.
#' @return 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.
#' @export
#' @import nsprcomp
#' @import stats
#' @import BiocParallel
#' @author 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')
#' ## Mapping CpGs into Pathways
#' 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(BiocParallel)
#' param <- MulticoreParam(workers = 10)
#' stage2data <- reconByUnsupervised(trainDataList=dataList, testDataList=NULL,
#' typeMode='regular', topPC=1,
#' innerCore=param, outFileA=NULL, outFileB=NULL)
#' print(dim(stage2data))
#' print(head(stage2data[,1:5]))
reconByUnsupervised <- function(trainDataList, testDataList, typeMode = "regular",
topPC = 1, innerCore = MulticoreParam(), outFileA = NULL, outFileB = NULL) {
reconDataAx <- c() ## for stage-2 training data
trainData <- trainDataList[[1]]
trainDataY <- trainData[, 1]
if (!is.null(testDataList)) {
reconDataBx <- c()
testData <- testDataList[[1]]
testDataY <- testData[, 1]
}
numlist <- seq_along(trainDataList)
predMat <- bplapply(numlist, function(i) {
trainData <- trainDataList[[i]]
trainDataX = trainData[, -1]
if (!is.null(testDataList)) {
testData <- testDataList[[i]]
testDataX = testData[, -1]
}
## remove zero variance columns from trainData
whNon0var <- apply(trainDataX, 2, var) != 0
if (sum(whNon0var) == 0) {
## if all features constant..
predA <- trainDataX[, 1]
if (!is.null(testDataList)) {
predB <- testDataX[, 1]
}
} else {
trainDataX2 <- trainDataX[, whNon0var]
if (!is.null(testDataList)) {
testX2 <- as.matrix(testDataX[, whNon0var])
}
if (typeMode == "regular") {
pca = prcomp(trainDataX2, center = TRUE, scale = TRUE)
predA <- pca$x[, seq_len(min(ncol(pca$x), topPC))]
if (!is.null(testDataList)) {
predB <- predict(pca, testX2)[, seq_len(min(ncol(pca$x), topPC))]
}
} else if (typeMode == "sparse") {
nspc <- nsprcomp(trainDataX2, ncomp = topPC)
predA <- nspc$x
if (!is.null(testDataList)) {
predB <- predict(nspc, testX2)[, topPC]
}
}
}
predA <- round(predA, 3)
pred <- predA
if (!is.null(testDataList)) {
predB <- round(predB, 3)
pred <- list(predA, predB)
}
pred
}, BPPARAM = innerCore)
if (!is.null(testDataList)) {
## PC1
predMatA <- lapply(predMat, function(data) {
data[[1]]
})
reconDataAx <- matrix(unlist(predMatA), ncol = length(predMatA))
row.names(reconDataAx) <- row.names(trainData) ##
colnames(reconDataAx) <- names(trainDataList)
reconDataA = data.frame(label = trainDataY, reconDataAx)
if (!is.null(outFileA)) {
saveRDS(reconDataA, file = outFileA)
}
## dataB
predMatB <- lapply(predMat, function(data) {
data[[2]]
})
reconDataBx <- matrix(unlist(predMatB), ncol = length(predMatB))
row.names(reconDataBx) <- row.names(testData) ##
colnames(reconDataBx) <- names(trainDataList)
reconDataB = data.frame(label = testDataY, reconDataBx)
if (!is.null(outFileB)) {
saveRDS(reconDataB, file = outFileB)
}
result <- list(reconDataA, reconDataB)
} else {
## Note 'predMat' is different from above
reconDataAx <- matrix(unlist(predMat), ncol = length(predMat))
row.names(reconDataAx) <- row.names(trainData) ##
colnames(reconDataAx) <- names(trainDataList)
reconDataA = data.frame(label = trainDataY, reconDataAx)
if (!is.null(outFileA)) {
saveRDS(reconDataA, file = outFileA)
}
result <- reconDataA
}
return(result)
}
###############################################################################
#' BioMM end-to-end prediction
#' @description
#' The BioMM framework uses two-stage machine learning models that can allow us
#' to integrate prior biological knowledge for end-to-end phenotype prediction.
#' @param 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.
#' @param 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.
#' @param pathlistDB A list of pathways with pathway IDs and their
#' corresponding genes ('entrezID' is used). This is only used for
#' pathway-based stratification (only \code{stratify} is 'pathway').
#' @param featureAnno The annotation data stored in a data.frame for probe
#' mapping. It must have at least two columns named 'ID' and 'entrezID'.
#' If it's NULL, then the input probe is from the transcriptomic data.
#' (Default: NULL)
#' @param restrictUp The upper-bound of the number of probes or genes in each
#' biological stratified block.
#' @param restrictDown The lower-bound of the number of probes or genes in
#' each biological stratified block.
#' @param minPathSize The minimal defined pathway size after mapping your
#' own data to GO database. This is only used for
#' pathway-based stratification (only \code{stratify} is 'pathway').
#' @param supervisedStage1 A logical value. If TRUE, then supervised learning
#' models are applied; if FALSE, unsupervised learning.
#' @param typePCA the type of PCA. Available options are c('regular',
#' 'sparse').
#' @param resample1 The resampling methods at stage-1. Valid options are
#' 'CV' and 'BS'. 'CV' for cross validation and 'BS' for bootstrapping
#' resampling. The default is 'BS'.
#' @param 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.
#' @param repeatA1 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'.
#' @param repeatA2 The number of repeats N is used during resampling
#' prediction. The default is 1 for 'CV'.
#' @param repeatB1 The number of repeats N is used for generating stage-2 test
#' data prediction scores. The default is 20.
#' @param repeatB2 The number of repeats N is used for test data prediction.
#' The default is 1.
#' @param nfolds The number of folds is defined for cross validation.
#' The default is 10.
#' @param FSmethod1 Feature selection methods at stage-1. Available options
#' are c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test',
#' 'posWilcox').
#' @param FSmethod2 Feature selection methods at stage-2. Features that are
#' positively associated with the outcome will be used.
#' @param cutP1 The cutoff used for p value thresholding at stage-1.
#' Commonly used cutoffs are c(0.5, 0.1, 0.05, 0.01, etc). If "FSmethod1" is NULL,
#' Then no cutoff is applied.
#' If FSmethod = "posTopCor", cutP is defined as the number of most
#' correlated features with 'fdr' = NULL.
#' @param cutP2 The cutoff used for p value thresholding at stage-2.
#' Commonly used cutoffs are c(0.5, 0.1, 0.05, 0.01, etc). If "FSmethod2" is NULL,
#' Then no cutoff is applied.
#' If FSmethod = "posTopCor", cutP is defined as the number of most
#' correlated features with 'fdr' = NULL.
#' @param fdr2 Multiple testing correction method at stage-2.
#' Available options are c(NULL, 'fdr', 'BH', 'holm', etc).
#' See also \code{\link[stats]{p.adjust}}. The default is NULL.
#' This option is useful particularly when large sets of pathways are investigated.
#' @param FScore The number of cores used for feature selection.
#' @param classifier Machine learning classifiers at both stages.
#' Available options are c('randForest', 'SVM', 'glmnet').
#' @param predMode The prediction mode at both stages. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A list of model parameters at both stages. The set of parameters
#' are different for each classifier. Please see the detailed parameters are
#' implemented for each individual classifier, e.g., 'baseRandForest()', 'baseSVM()',
#' and 'baseGLMnet()'.
#' @param innerCore The number of cores used for computation. It needs to be reconciled
#' with "FScore" depending on the number of cores available.
#' @return The CV or BS predicted score for the training data and
#' test set predicted score if \code{testData} is given.
#' @details Stage-2 training data can be learned either using bootstrapping
#' or cross validation resampling methods in the supervised learning settting.
#' Stage-2 test data is learned via independent test set prediction.
#' @export
#' @references Chen, J., & Schwarz, E. (2017). BioMM: Biologically-informed
#' Multi-stage Machine learning for identification of epigenetic fingerprints.
#' arXiv preprint arXiv:1712.00336.
#' @references Perlich, C., & Swirszcz, G. (2011). On cross-validation and
#' stacking: Building seemingly predictive models on random data. ACM SIGKDD
#' Explorations Newsletter, 12(2), 11-15.
#' @seealso \code{\link{reconBySupervised}}; \code{\link{reconByUnsupervised}};
#' \code{\link{BioMMstage2pred}}
#' @examples
#' ## Load data
#' methylfile <- system.file('extdata', 'methylData.rds', package='BioMM')
#' methylData <- readRDS(methylfile)
#' testData <- NULL
#' ## Annotation file
#' probeAnnoFile <- system.file('extdata', 'cpgAnno.rds', package='BioMM')
#' probeAnno <- readRDS(file=probeAnnoFile)
#' golist <- readRDS(system.file("extdata", "goDB.rds", package="BioMM"))
#' pathlistDB <- golist[1:100]
#' supervisedStage1=TRUE
#' classifier <- 'randForest'
#' predMode <- 'classification'
#' paramlist <- list(ntree=300, nthreads=30)
#' library(BiocParallel)
#' library(ranger)
#' param1 <- MulticoreParam(workers = 2)
#' param2 <- MulticoreParam(workers = 20)
#' ## Not Run
#' ## result <- BioMM(trainData=methylData, testData=NULL,
#' ## pathlistDB, featureAnno=probeAnno,
#' ## restrictUp=200, restrictDown=10, minPathSize=10,
#' ## supervisedStage1, typePCA='regular',
#' ## resample1='BS', resample2='CV', dataMode="allTrain",
#' ## repeatA1=20, repeatA2=1, repeatB1=20, repeatB2=1,
#' ## nfolds=10, FSmethod1=NULL, FSmethod2=NULL,
#' ## cutP1=0.1, cutP2=0.1, fdr2=NULL, FScore=param1,
#' ## classifier, predMode, paramlist, innerCore=param2)
#' ## if (is.null(testData)) {
#' ## predY <- result
#' ## trainDataY <- methylData[,1]
#' ## metricCV <- getMetrics(dataY = trainDataY, predY)
#' ## message("Cross-validation prediction performance:")
#' ## print(metricCV)
#' ## } else if (!is.null(testData)){
#' ## trainDataY <- methylData[,1]
#' ## testDataY <- testData[,1]
#' ## cvYscore <- result[[1]]
#' ## testYscore <- result[[2]]
#' ## metricCV <- getMetrics(dataY = trainDataY, cvYscore)
#' ## metricTest <- getMetrics(dataY = testDataY, testYscore)
#' ## message("Cross-validation performance:")
#' ## print(metricCV)
#' ## message("Test set prediction performance:")
#' ## print(metricTest)
#' ## }
BioMM <- function(trainData, testData, pathlistDB, featureAnno,
restrictUp, restrictDown, minPathSize, supervisedStage1 = TRUE,
typePCA, resample1 = "BS", dataMode = "allTrain",
repeatA1 = 100, repeatA2 = 1, repeatB1 = 20, repeatB2 = 1,
nfolds = 10, FSmethod1, FSmethod2,
cutP1, cutP2, fdr2, FScore = MulticoreParam(), classifier,
predMode, paramlist, innerCore = MulticoreParam()) {
trainDataList <- omics2pathlist(data=trainData, pathlistDB,
featureAnno, restrictUp,
restrictDown, minPathSize)
testDataList <- omics2pathlist(data=testData, pathlistDB,
featureAnno, restrictUp,
restrictDown, minPathSize)
## generation of stage-2 data
if (supervisedStage1 == TRUE) {
stage2data <- reconBySupervised(trainDataList = trainDataList,
testDataList = testDataList, resample = resample1, dataMode,
repeatA = repeatA1, repeatB = repeatB1, nfolds,
FSmethod = FSmethod1, cutP = cutP1, fdr = NULL, FScore = FScore,
classifier = classifier, predMode = predMode,
paramlist = paramlist, innerCore = innerCore,
outFileA = NULL, outFileB = NULL)
} else {
stage2data <- reconByUnsupervised(trainDataList = trainDataList,
testDataList = testDataList, typeMode = typePCA, topPC = 1,
innerCore = innerCore, outFileA = NULL, outFileB = NULL)
}
## if testData provided
trainData2 <- stage2data[[1]]
testData2 <- stage2data[[2]]
datalist <- getDataByFilter(trainData2, testData2, FSmethod = "positive",
cutP = 0.1, fdr = NULL, FScore = FScore)
## include the label
trainPos2 <- datalist[[1]]
message(paste0("Number of positive blocks: ", ncol(trainPos2) - 1))
## If no positive features
if (is.null(trainPos2)) {
message("Warning: No positive/useful stage-2 features!!")
result <- data.frame(AUC = 0.5, ACC = 0.5, R2 = 0)
} else if (!is.null(trainPos2)){
## make prediction
result <- BioMMstage2pred(trainData = trainData2, testData = testData2,
dataMode, repeatA = repeatA2,
repeatB = repeatB2, nfolds, FSmethod = FSmethod2, cutP = cutP2,
fdr = fdr2, FScore = FScore, classifier = classifier,
predMode = predMode, paramlist = paramlist, innerCore = innerCore)
}
return(result)
}
transbioZI/BioMMex documentation built on Jan. 27, 2023, 4:14 a.m.
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Defines functions BioMM reconByUnsupervised BioMMstage2pred reconBySupervised predByCV predByBS predByFS baseModel baseGLMnet baseSVM baseRandForest
Documented in baseGLMnet baseModel baseRandForest baseSVM BioMM BioMMstage2pred predByBS predByCV predByFS reconBySupervised reconByUnsupervised
# File : BioMM.R Author: Junfang Chen
###############################################################################
#' Prediction by random forest
#'
#' @description
#' Prediction by random forest with different settings: 'probability',
#' 'classification' and 'regression'.
#' @param 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.
#' @param 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.
#' @param predMode The prediction mode. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' The valid option: list(ntree, nthreads). 'ntree' is the number of trees
#' used. The defaul is 2000. 'nthreads' is the number of threads used for
#' computation. The default is 20.
#' @return The predicted output for the test data.
#' @import ranger
#' @export
#' @author Junfang Chen
#' @examples
#' ## Load data
#' methylfile <- system.file('extdata', 'methylData.rds', package='BioMM')
#' methylData <- readRDS(methylfile)
#' dataY <- methylData[,1]
#' ## test a subset of genome-wide methylation data at random
#' methylSub <- data.frame(label=dataY, methylData[,c(2:2001)])
#' trainIndex <- sample(nrow(methylSub), 12)
#' trainData = methylSub[trainIndex,]
#' testData = methylSub[-trainIndex,]
#' library(ranger)
#' predY <- baseRandForest(trainData, testData,
#' predMode='classification',
#' paramlist=list(ntree=300, nthreads=20))
#' testY <- testData[,1]
#' accuracy <- classifiACC(dataY=testY, predY=predY)
#' print(accuracy)
baseRandForest <- function(trainData, testData, predMode = c("classification",
"probability", "regression"), paramlist = list(ntree = 2000, nthreads = 20)) {
predMode <- match.arg(predMode)
## input parameters
if (!is.element("ntree", names(paramlist))) {
stop(" 'ntree' is missing in the 'paramlist'!")
}
if (!is.element("nthreads", names(paramlist))) {
stop(" 'nthreads' is missing in the 'paramlist'!")
}
ntree <- paramlist$ntree
nthreads <- paramlist$nthreads
if (predMode == "probability") {
trainData$label <- as.factor(trainData$label)
model <- ranger(label ~ ., data = trainData, num.trees = ntree,
num.threads = nthreads, write.forest = TRUE, probability = TRUE)
yhatClassProb <- predictions(predict(model, testData))
predTest <- round(yhatClassProb[, 2], 3)
} else if (predMode == "classification") {
trainData$label <- as.factor(trainData$label)
model <- ranger(label ~ ., data = trainData, num.trees = ntree,
num.threads = nthreads, write.forest = TRUE, classification = TRUE)
predTest <- predictions(predict(model, testData))
predTest <- as.numeric(predTest) - 1
} else if (predMode == "regression") {
model <- ranger(label ~ ., data = trainData, num.trees = ntree,
num.threads = nthreads)
predTest <- predictions(predict(model, testData))
predTest <- round(predTest, 3)
}
return(predTest)
}
###############################################################################
#' Prediction by SVM
#'
#' @description
#' Prediction by support vector machine (SVM) with two different settings:
#' 'classification' and 'regression'.
#' @param 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.
#' @param 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.
#' @param predMode The prediction mode. Available options are
#' c('classification', 'probability', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' The valid option: list(kernel, gamma, cost, tuneP).
#' \enumerate{
#' \item 'tuneP': a logical value indicating if hyperparameter tuning
#' should be conducted or not. The default is FALSE.
#' \item 'kernel': options are c('linear', 'polynomial', 'radial',
#' 'sigmoid'). The defaut is 'radial'.
#' \item 'gamma': the parameter needed for all kernels except 'linear'.
#' If tuneP is TRUE, more than one value is suggested.
#' \item 'cost': is the cost of constraints violation.
#' If tuneP is TRUE, more than one value is suggested.
#' }
#' @return The predicted output for the test data.
#' @details Hyperparameter tuning is recommended in many biological data
#' mining applications. The best parameters can be determined via an internal
#' cross validation.
#' @import e1071
#' @export
#' @author Junfang Chen
##' @seealso \code{\link[e1071]{svm}}
#' @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), 12)
#' trainData = methylSub[trainIndex,]
#' testData = methylSub[-trainIndex,]
#' library(e1071)
#' predY <- baseSVM(trainData, testData,
#' predMode='classification',
#' paramlist=list(tuneP=FALSE, kernel='radial',
#' gamma=10^(-3:-1), cost=10^(-3:1)))
#' testY <- testData[,1]
#' accuracy <- classifiACC(dataY=testY, predY=predY)
#' print(accuracy)
baseSVM <- function(trainData, testData,
predMode = c("classification", "probability", "regression"),
paramlist = list(tuneP = TRUE, kernel = "radial", gamma = 10^(-3:-1),
cost = 10^(-2:2))) {
predMode <- match.arg(predMode)
## input parameters
if (!is.element("tuneP", names(paramlist))) {
stop(" 'tuneP' is missing in the 'paramlist'!")
}
if (!is.element("kernel", names(paramlist))) {
stop(" 'kernel' is missing in the 'paramlist'!")
}
if (!is.element("gamma", names(paramlist))) {
stop(" 'gamma' is missing in the 'paramlist'!")
}
if (!is.element("cost", names(paramlist))) {
stop(" 'cost' is missing in the 'paramlist'!")
}
tuneP <- paramlist$tuneP
kernel <- paramlist$kernel
gamma <- paramlist$gamma
cost <- paramlist$cost
if (tuneP == TRUE) {
kernel <- paramlist$kernel
}
if (tuneP == TRUE) {
gamma <- paramlist$gamma
}
if (tuneP == TRUE) {
cost <- paramlist$cost
}
if (predMode == "classification" || predMode == "probability") {
type <- "C-classification" ##
trainData$label <- as.factor(trainData$label)
} else if (predMode == "regression") {
type <- "eps-regression"
if (is.factor(trainData$label)) {
trainData$label <- as.numeric(trainData$label) - 1
}
}
if (predMode == "probability") {
if (tuneP == TRUE) {
## use internal CV
tuneOut <- tune.svm(label ~ ., data = trainData, type, kernel,
gamma, cost, probability = TRUE)
model <- tuneOut$best.model
} else {
## no internal CV required
model <- svm(label ~ ., data = trainData, probability = TRUE)
}
predTest2 <- predict(model, testData, probability = TRUE)
predTest <- round(attr(predTest2, "probabilities")[, 2], 3)
} else {
if (tuneP == TRUE) {
## use internal CV
tuneOut <- tune.svm(label ~ ., data = trainData, type, kernel,
gamma, cost)
model <- tuneOut$best.model
} else {
## no CV required
model <- svm(label ~ ., data = trainData)
}
predTest <- predict(model, testData)
if (predMode == "classification") {
predTest <- as.numeric(predTest) - 1
}
}
return(predTest)
}
###############################################################################
#' Prediction by generalized linear regression models
#'
#' @description
#' Prediction by generalized regression models with lasso or elastic net
#' regularization.
#' @param trainData The input training dataset. The first column is named
#' the 'label'.
#' @param testData The input test dataset. The first column is named
#' the 'label'.
#' @param predMode The prediction mode. Available options are
#' c('classification', 'probability', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' The valid option: list(family, alpha, typeMeasure, typePred).
#' \enumerate{
#' \item 'family': Response type: 'gaussian','binomial','poisson',
#' 'multinomial','cox','mgaussian'. (Default: 'binomial')
#' \item 'alpha': The elastic net mixing parameter, with 0<=alpha<= 1.
#' \item 'typeMeasure': error metrics for internal cross-validation.
#' 'mse' uses squared loss;
#' 'deviance' uses actual deviance;
#' 'mae' uses mean absolute error;
#' 'class' gives misclassification error;
#' 'auc' (for two-class logistic regression ONLY)
#' gives area under the ROC curve.
#' \item 'typePred': The type of prediction: 'response' and 'class'.
#' (Default: 'class' for binary classification)
#' }
#' @return The predicted output for the test data.
#' @export
#' @import glmnet
#' @author Junfang Chen
#' @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(glmnet)
#' ## classification
#' predY <- baseGLMnet(trainData, testData,
#' predMode='classification',
#' paramlist=list(family='binomial', alpha=0.5,
#' typeMeasure='mse', typePred='class'))
#' testY <- testData[,1]
#' accuracy <- classifiACC(dataY=testY, predY=predY)
#' print(accuracy)
baseGLMnet <- function(trainData, testData,
predMode = c("classification", "probability", "regression"),
paramlist = list(family = "binomial", alpha = 0.5, typeMeasure = "mse",
typePred = "class")) {
predMode <- match.arg(predMode)
## input parameters
if (!is.element("family", names(paramlist))) {
stop(" 'family' is missing in the 'paramlist'!")
}
if (!is.element("alpha", names(paramlist))) {
stop(" 'alpha' is missing in the 'paramlist'!")
}
if (!is.element("typeMeasure", names(paramlist))) {
stop(" 'typeMeasure' is missing in the 'paramlist'!")
}
if (!is.element("typePred", names(paramlist))) {
stop(" 'typePred' is missing in the 'paramlist'!")
}
family <- paramlist$family
alpha <- paramlist$alpha
typeMeasure <- paramlist$typeMeasure
typePred <- paramlist$typePred
trainDataY <- trainData$label
## To avoid 'X should be a matrix with 2 or more cols'
if (ncol(trainData) == 2) {
trainData$one <- rep(1, nrow(trainData))
testData$one <- rep(1, nrow(testData))
trainDataX <- trainData[, -1]
testDataX <- testData[, -1]
} else {
trainDataX <- trainData[, -1]
testDataX <- testData[, -1]
}
cvfit <- cv.glmnet(as.matrix(trainDataX), trainDataY, family = family,
type.measure = typeMeasure, nfolds = 10)
model <- glmnet(as.matrix(trainDataX), trainDataY, family = family,
alpha = alpha, lambda = cvfit$lambda.min)
yhat <- predict(model, as.matrix(testDataX), type = typePred)
if (predMode == "classification") {
## set type='class'
predTest = as.numeric(yhat)
} else if (predMode == "probability" || predMode == "regression") {
predTest = round(yhat[, 1], 3)
}
return(predTest)
}
###############################################################################
#' Base supervised machine learning models for prediction
#'
#' @description
#' Prediction using different supervised machine learning models.
#' @param 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.
#' @param 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.
#' @param classifier Machine learning classifiers.
#' Available options are c('randForest', 'SVM', 'glmnet').
#' @param predMode The prediction mode. Available options are
#' c('classification', 'probability', 'regression').
#' 'probability' is currently only for 'randForest'.
#' @param paramlist A set of model parameters defined in an R list object.
#' See more details for each individual model.
#' @return Based on a given machine learning, the predicted score/output will be
#' estimated for the test data.
#' @export
#' @author Junfang Chen
#' @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)
#' set.seed(123)
#' predY <- baseModel(trainData, testData,
#' classifier='randForest',
#' predMode='classification',
#' paramlist=list(ntree=300, nthreads=20))
#' print(table(predY))
#' testY <- testData[,1]
#' accuracy <- classifiACC(dataY=testY, predY=predY)
#' print(accuracy)
baseModel <- function(trainData, testData,
classifier = c("randForest", "SVM", "glmnet"),
predMode = c("classification", "probability", "regression"), paramlist) {
if (colnames(trainData)[1] != "label") {
stop("The first column of the 'trainData' must be the 'label'!")
}
if (colnames(testData)[1] != "label") {
stop("The first column of the 'testData' must be the 'label'!")
}
classifier <- match.arg(classifier)
predMode <- match.arg(predMode)
if (classifier == "randForest") {
predTest <- baseRandForest(trainData, testData, predMode, paramlist)
} else if (classifier == "SVM") {
predTest <- baseSVM(trainData, testData, predMode, paramlist)
} else if (classifier == "glmnet") {
predTest <- baseGLMnet(trainData, testData, predMode, paramlist)
}
return(predTest)
}
###############################################################################
#' Prediction by supervised machine learning along with feature selection
#'
#' @description
#' Prediction by supervised machine learning along with feature selection.
#' @param 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.
#' @param 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.
#' @param FSmethod Feature selection methods. Available options are
#' c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox',
#' or 'top10pCor').
#' @param 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.
#' @param fdr Multiple testing correction method. Available options are
#' c(NULL, 'fdr', 'BH', 'holm', etc.).
#' See also \code{\link[stats]{p.adjust}}. The default is NULL.
#' @param FScore The number of cores used for feature selection.
#' @param classifier Machine learning classifiers.
#' Available options are c('randForest', 'SVM', 'glmnet').
#' @param predMode The prediction mode. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' @return The predicted output for the test data.
#' @details If no feature selected or just one selected feature,
#' then top 10% outcome-associated features will be selected by default.
#' @export
#' @author Junfang Chen
#' @seealso \code{\link{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)
predByFS <- function(trainData, testData, FSmethod, cutP, fdr,
FScore = MulticoreParam(), classifier, predMode, paramlist) {
datalist <- getDataByFilter(trainData, testData, FSmethod, cutP, fdr, FScore)
## include the label
trainDataSub <- datalist[[1]]
testDataSub <- datalist[[2]]
## If no selected features or just one selected feature.
if (is.null(trainDataSub) || ncol(trainDataSub) == 2) {
datalist <- getDataByFilter(trainData, testData, FSmethod = "top10pCor",
cutP, fdr, FScore)
trainDataSub <- datalist[[1]]
testDataSub <- datalist[[2]]
predTest <- baseModel(trainData = trainDataSub, testData = testDataSub,
classifier, predMode, paramlist)
} else if (!is.null(trainDataSub)) {
predTest <- baseModel(trainData = trainDataSub, testData = testDataSub,
classifier, predMode, paramlist)
} else {
message("Error: Input data is wrong!")
}
if (is.factor(predTest)) {
predTest <- as.numeric(predTest) - 1
}
return(predTest)
}
###############################################################################
#' Bootstrap resampling prediction via supervised machine learning with
#' feature selection
#'
#' @description
#' Prediction via supervised machine learning using bootstrap resampling
#' along with feature selection methods.
#' @param 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.
#' @param 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.
#' @param 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.
#' @param repeats The number of repeats used for boostrapping.
#' @param FSmethod Feature selection methods. Available options are
#' c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox',
#' or 'top10pCor').
#' @param 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.
#' @param fdr Multiple testing correction method. Available options are
#' c(NULL, 'fdr', 'BH', 'holm', etc).
#' See also \code{\link[stats]{p.adjust}}. The default is NULL.
#' If FSmethod = "posTopCor", cutP is defined as the number of most
#' correlated features with 'fdr' = NULL.
#' @param FScore The number of cores used for feature selection
#' if parallel computing needed.
#' @param classifier Machine learning classifiers.
#' @param predMode The prediction mode. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' @param innerCore The number of cores used for computation.
#' @return The predicted output for the test data.
#' @export
#' @import BiocParallel
#' @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)
predByBS <- function(trainData, testData, dataMode, repeats, FSmethod, cutP,
fdr, FScore = MulticoreParam(), classifier, predMode, paramlist,
innerCore = MulticoreParam()) {
if (is.null(testData)) {
## BS only applied for training data
data <- trainData
predTmp <- rep(NA, nrow(data))
replist <- seq_len(repeats)
predTmpList <- bplapply(replist, function(reps) {
trainIndex <- unique(sample(nrow(data), replace = TRUE))
testIndex <- setdiff(seq_len(nrow(data)), trainIndex)
trainData <- data[trainIndex, ]
testData <- data[testIndex, ]
## OOB predicted scores
predTmp[testIndex] <- predByFS(trainData, testData, FSmethod, cutP,
fdr, FScore, classifier, predMode, paramlist)
predTmp
}, BPPARAM = innerCore)
predTest <- round(rowMeans(do.call(cbind, predTmpList), na.rm = TRUE),
3)
naCount <- sum(is.na(predTest))
## If not enough bootstrapping repeats
if (naCount > 0) {
message(paste0("Warning: ", naCount, " NA(s) in predicted testY!"))
}
} else if (!is.null(testData)) {
## repeated prediction on test data
if (dataMode == "subTrain") {
data <- trainData
predTmp <- rep(NA, nrow(data))
replist <- seq_len(repeats)
predTmpList <- bplapply(replist, function(reps) {
trainIndex <- unique(sample(nrow(data), replace = TRUE))
trainData <- data[trainIndex, ]
predTmp <- predByFS(trainData, testData, FSmethod, cutP, fdr,
FScore, classifier, predMode, paramlist)
predTmp
}, BPPARAM = innerCore)
predTest <- round(rowMeans(do.call(cbind, predTmpList)), 3)
} else if (dataMode == "allTrain") {
predTmp <- rep(NA, nrow(testData))
replist <- seq_len(repeats)
predTmpList <- bplapply(replist, function(reps) {
predTmp <- predByFS(trainData, testData, FSmethod, cutP, fdr,
FScore, classifier, predMode, paramlist)
predTmp
}, BPPARAM = innerCore)
predTest <- round(rowMeans(do.call(cbind, predTmpList)), 3)
}
} else {
message("Input data is wrong!")
}
if (predMode == "classification") {
predTest <- ifelse(predTest >= 0.5, 1, 0)
}
return(predTest)
}
###############################################################################
#' Cross validation prediction by supervised machine learning and feature
#' selection
#' @description
#' Prediction by supervised machine learning models using cross validation
#' along with feature selection methods.
#' @param data The input dataset. The first column
#' is the label or the output. For binary classes,
#' 0 and 1 are used to indicate the class member.
#' @param repeats The number of repeats used for cross validation.
#' Repeated cross validation is performed if N >=2.
#' @param nfolds The number of folds is defined for cross validation.
#' @param FSmethod Feature selection methods. Available options are
#' c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox',
#' or 'top10pCor').
#' @param 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.
#' If FSmethod = "posTopCor", cutP is defined as the number of most
#' correlated features with 'fdr' = NULL.
#' @param fdr Multiple testing correction method. Available options are
#' c(NULL, 'fdr', 'BH', 'holm', etc).
#' See also \code{\link[stats]{p.adjust}}. The default is NULL.
#' @param FScore The number of cores used for feature selection if parallel
#' computing needed.
#' @param classifier Machine learning classifiers.
#' @param predMode The prediction mode. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' @param innerCore The number of cores used for computation.
#' @return The predicted cross validation output.
#' @export
#' @import BiocParallel
#' @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)])
#' library(ranger)
#' library(BiocParallel)
#' param1 <- MulticoreParam(workers = 1)
#' param2 <- MulticoreParam(workers = 20)
#' predY <- predByCV(methylSub, repeats=1, nfolds=10,
#' FSmethod=NULL, cutP=0.1,
#' fdr=NULL, FScore=param1,
#' classifier='randForest',
#' predMode='classification',
#' paramlist=list(ntree=300, nthreads=1),
#' innerCore=param2)
#' dataY <- methylData[,1]
#' accuracy <- classifiACC(dataY=dataY, predY=predY)
#' print(accuracy)
predByCV <- function(data, repeats, nfolds, FSmethod, cutP, fdr, FScore = MulticoreParam(),
classifier, predMode, paramlist, innerCore = MulticoreParam()) {
cvMat <- c()
replist <- seq_len(repeats)
for (reps in replist) {
foldlists <- split(sample(nrow(data)), rep(seq_len(nfolds),
length = nrow(data)))
cvY <- rep(NA, nrow(data))
cvEstimate <- bplapply(foldlists, function(fold) {
trainData <- data[-fold, ]
testData <- data[fold, ]
predTest <- predByFS(trainData, testData, FSmethod, cutP, fdr,
FScore, classifier, predMode, paramlist)
}, BPPARAM = innerCore)
cvY[unlist(foldlists)] <- unlist(cvEstimate)
cvMat <- cbind(cvMat, cvY)
}
if (repeats == 1) {
predTest <- cvMat[, 1]
} else {
## average over the repeats
predTest <- round(rowMeans(cvMat, na.rm = TRUE), 3)
}
if (predMode == "classification") {
predTest <- ifelse(predTest >= 0.5, 1, 0)
}
return(predTest)
}
###############################################################################
#' Reconstruct stage-2 data by supervised machine learning prediction
#' @description
#' Reconstruct stage-2 data by supervised machine learning prediction.
#' @param trainDataList The input training data list containing ordered
#' collections of matrices.
#' @param testDataList The input test data list containing ordered collections
#' of matrices.
#' @param resample The resampling methods. Valid options are 'CV' and 'BS'.
#' 'CV' for cross validation and 'BS' for bootstrapping resampling.
#' The default is 'BS'.
#' @param dataMode The mode of data used. 'subTrain' or 'allTrain'.
#' @param 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'.
#' @param repeatB The number of repeats N is used for generating test data
#' prediction scores.
#' @param nfolds The number of folds is defined for cross validation.
#' @param FSmethod Feature selection methods. Available options are
#' c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox',
#' or 'top10pCor').
#' @param 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.
#' If FSmethod = "posTopCor", cutP is defined as the number of most
#' correlated features with 'fdr' = NULL.
#' @param fdr Multiple testing correction method. Available options are
#' c(NULL, 'fdr', 'BH', 'holm', etc).
#' See also \code{\link[stats]{p.adjust}}. The default is NULL.
#' @param FScore The number of cores used for feature selection, if parallel
#' computing needed.
#' @param classifier Machine learning classifiers.
#' @param predMode The prediction mode. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' @param innerCore The number of cores used for computation.
#' @param 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.
#' @param 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.
#' @return 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.
#' @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.
#' @export
#' @import stats
#' @import BiocParallel
#' @author 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]))
reconBySupervised <- function(trainDataList, testDataList, resample = "BS",
dataMode, repeatA, repeatB, nfolds, FSmethod, cutP, fdr, FScore = MulticoreParam(), classifier,
predMode, paramlist, innerCore = MulticoreParam(), outFileA = NULL, outFileB = NULL) {
reconDataAx <- c()
reconDataBx <- c()
for (i in seq_along(trainDataList)) {
## for each block
trainData = trainDataList[[i]]
dataAy <- trainData[, 1]
message(paste0('Block:', i))
message(paste0('Block_numFeature: ', c(ncol(trainData)-1)))
if (resample == "CV") {
predA <- predByCV(data = trainData, repeats = repeatA, nfolds,
FSmethod, cutP, fdr, FScore, classifier, predMode, paramlist,
innerCore)
} else if (resample == "BS") {
predA <- predByBS(trainData, testData = NULL, dataMode,
repeats = repeatA, FSmethod, cutP, fdr, FScore, classifier,
predMode, paramlist, innerCore)
}
reconDataAx <- cbind(reconDataAx, predA)
if (!is.null(testDataList)) {
## ind. prediction
testData = testDataList[[i]]
dataBy <- testData[, 1]
predB <- predByBS(trainData, testData, dataMode, repeats = repeatB,
FSmethod, cutP, fdr, FScore, classifier, predMode, paramlist,
innerCore)
reconDataBx <- cbind(reconDataBx, predB)
}
}
row.names(reconDataAx) <- row.names(trainData) ##
reconDataA <- data.frame(label = dataAy, reconDataAx)
colnames(reconDataA) <- c("label", names(trainDataList))
## Provide fileName
if (!is.null(outFileA)) {
saveRDS(reconDataA, file = outFileA)
}
if (!is.null(testDataList)) {
## ind. prediction
row.names(reconDataBx) <- row.names(testData) ##
reconDataB <- data.frame(label = dataBy, reconDataBx)
colnames(reconDataB) <- c("label", names(testDataList))
if (!is.null(outFileB)) {
saveRDS(reconDataB, file = outFileB)
}
result <- list(reconDataA, reconDataB)
} else {
result <- reconDataA
}
return(result)
}
###############################################################################
#' Prediction performance for stage-2 data using supervised machine learning
#' @description
#' Prediction performance for reconstructed stage-2 data using supervised
#' machine learning with feature selection methods.
#' @param trainData The input training dataset (stage-2 data). The first column
#' is the label or the output. For binary classes,
#' 0 and 1 are used to indicate the class member.
#' @param testData The input test dataset (stage-2 data). The first column
#' is the label or the output. For binary classes,
#' 0 and 1 are used to indicate the class member.
#' @param dataMode The mode of data used. 'subTrain' or 'allTrain'.
#' @param repeatA The number of repeats N is used during resampling prediction.
#' The default is 1.
#' @param repeatB The number of repeats N is used for test data prediction.
#' The default is 1.
#' @param nfolds The number of folds is defined for cross validation.
#' @param FSmethod Feature selection methods. Available options are
#' c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox',
#' 'top10pCor', 'posTopCor').
#' @param 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.
#' If FSmethod = "posTopCor", cutP is then defined as the number of most
#' correlated features with 'fdr' = NULL.
#' @param fdr Multiple testing correction method. Available options are
#' c(NULL, 'fdr', 'BH', 'holm', etc).
#' See also \code{\link[stats]{p.adjust}}. The default is NULL.
#' @param FScore The number of cores used for feature selection if parallel
#' computing needed.
#' @param classifier Machine learning classifiers.
#' @param predMode The prediction mode. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A set of model parameters defined in an R list object.
#' @param innerCore The number of cores used for computation.
#' @return The CV or BS predicted score for stage-2 training data and
#' test set predicted score for stage-2 test data if the test set is
#' given.
#' @details Stage-2 prediction is performed typically using positively
#' correlated features. Since negative associations likely reflect random
#' effects in the underlying data
#' @export
#' @import stats
#' @import utils
#' @references Perlich, C., & Swirszcz, G. (2011). On cross-validation and
#' stacking: Building seemingly predictive models on random data. ACM SIGKDD
#' Explorations Newsletter, 12(2), 11-15.
#' @author Junfang Chen
BioMMstage2pred <- function(trainData, testData, dataMode,
repeatA = 1, repeatB = 1, nfolds, FSmethod, cutP, fdr,
FScore = MulticoreParam(), classifier,
predMode, paramlist, innerCore = MulticoreParam() ) {
if (is.factor(testData$label)) {
testData$label <- as.numeric(testData$label) - 1
}
testY <- testData$label
predY <- predByBS(trainData, testData, dataMode, repeats = repeatB,
FSmethod, cutP, fdr, FScore, classifier, predMode, paramlist,
innerCore)
result <- predY
return(result)
}
###############################################################################
#' Reconstruct stage-2 data by PCA
#' @description
#' Stage-2 data reconstruction by regular or sparse constrained principal
#' component analysis (PCA).
#' @param trainDataList The input training data list containing ordered
#' collections of matrices.
#' @param testDataList The input test data list containing ordered collections
#' of matrices.
#' @param typeMode The type of PCA prediction mode. Available options are
#' c('regular', 'sparse').
#' (Default: regular)
#' @param topPC The number of top PCs selected. The default is 1,
#' i.e. the first PC.
#' @param innerCore The number of cores used for computation.
#' @param 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.
#' @param 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.
#' @return 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.
#' @export
#' @import nsprcomp
#' @import stats
#' @import BiocParallel
#' @author 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')
#' ## Mapping CpGs into Pathways
#' 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(BiocParallel)
#' param <- MulticoreParam(workers = 10)
#' stage2data <- reconByUnsupervised(trainDataList=dataList, testDataList=NULL,
#' typeMode='regular', topPC=1,
#' innerCore=param, outFileA=NULL, outFileB=NULL)
#' print(dim(stage2data))
#' print(head(stage2data[,1:5]))
reconByUnsupervised <- function(trainDataList, testDataList, typeMode = "regular",
topPC = 1, innerCore = MulticoreParam(), outFileA = NULL, outFileB = NULL) {
reconDataAx <- c() ## for stage-2 training data
trainData <- trainDataList[[1]]
trainDataY <- trainData[, 1]
if (!is.null(testDataList)) {
reconDataBx <- c()
testData <- testDataList[[1]]
testDataY <- testData[, 1]
}
numlist <- seq_along(trainDataList)
predMat <- bplapply(numlist, function(i) {
trainData <- trainDataList[[i]]
trainDataX = trainData[, -1]
if (!is.null(testDataList)) {
testData <- testDataList[[i]]
testDataX = testData[, -1]
}
## remove zero variance columns from trainData
whNon0var <- apply(trainDataX, 2, var) != 0
if (sum(whNon0var) == 0) {
## if all features constant..
predA <- trainDataX[, 1]
if (!is.null(testDataList)) {
predB <- testDataX[, 1]
}
} else {
trainDataX2 <- trainDataX[, whNon0var]
if (!is.null(testDataList)) {
testX2 <- as.matrix(testDataX[, whNon0var])
}
if (typeMode == "regular") {
pca = prcomp(trainDataX2, center = TRUE, scale = TRUE)
predA <- pca$x[, seq_len(min(ncol(pca$x), topPC))]
if (!is.null(testDataList)) {
predB <- predict(pca, testX2)[, seq_len(min(ncol(pca$x), topPC))]
}
} else if (typeMode == "sparse") {
nspc <- nsprcomp(trainDataX2, ncomp = topPC)
predA <- nspc$x
if (!is.null(testDataList)) {
predB <- predict(nspc, testX2)[, topPC]
}
}
}
predA <- round(predA, 3)
pred <- predA
if (!is.null(testDataList)) {
predB <- round(predB, 3)
pred <- list(predA, predB)
}
pred
}, BPPARAM = innerCore)
if (!is.null(testDataList)) {
## PC1
predMatA <- lapply(predMat, function(data) {
data[[1]]
})
reconDataAx <- matrix(unlist(predMatA), ncol = length(predMatA))
row.names(reconDataAx) <- row.names(trainData) ##
colnames(reconDataAx) <- names(trainDataList)
reconDataA = data.frame(label = trainDataY, reconDataAx)
if (!is.null(outFileA)) {
saveRDS(reconDataA, file = outFileA)
}
## dataB
predMatB <- lapply(predMat, function(data) {
data[[2]]
})
reconDataBx <- matrix(unlist(predMatB), ncol = length(predMatB))
row.names(reconDataBx) <- row.names(testData) ##
colnames(reconDataBx) <- names(trainDataList)
reconDataB = data.frame(label = testDataY, reconDataBx)
if (!is.null(outFileB)) {
saveRDS(reconDataB, file = outFileB)
}
result <- list(reconDataA, reconDataB)
} else {
## Note 'predMat' is different from above
reconDataAx <- matrix(unlist(predMat), ncol = length(predMat))
row.names(reconDataAx) <- row.names(trainData) ##
colnames(reconDataAx) <- names(trainDataList)
reconDataA = data.frame(label = trainDataY, reconDataAx)
if (!is.null(outFileA)) {
saveRDS(reconDataA, file = outFileA)
}
result <- reconDataA
}
return(result)
}
###############################################################################
#' BioMM end-to-end prediction
#' @description
#' The BioMM framework uses two-stage machine learning models that can allow us
#' to integrate prior biological knowledge for end-to-end phenotype prediction.
#' @param 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.
#' @param 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.
#' @param pathlistDB A list of pathways with pathway IDs and their
#' corresponding genes ('entrezID' is used). This is only used for
#' pathway-based stratification (only \code{stratify} is 'pathway').
#' @param featureAnno The annotation data stored in a data.frame for probe
#' mapping. It must have at least two columns named 'ID' and 'entrezID'.
#' If it's NULL, then the input probe is from the transcriptomic data.
#' (Default: NULL)
#' @param restrictUp The upper-bound of the number of probes or genes in each
#' biological stratified block.
#' @param restrictDown The lower-bound of the number of probes or genes in
#' each biological stratified block.
#' @param minPathSize The minimal defined pathway size after mapping your
#' own data to GO database. This is only used for
#' pathway-based stratification (only \code{stratify} is 'pathway').
#' @param supervisedStage1 A logical value. If TRUE, then supervised learning
#' models are applied; if FALSE, unsupervised learning.
#' @param typePCA the type of PCA. Available options are c('regular',
#' 'sparse').
#' @param resample1 The resampling methods at stage-1. Valid options are
#' 'CV' and 'BS'. 'CV' for cross validation and 'BS' for bootstrapping
#' resampling. The default is 'BS'.
#' @param 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.
#' @param repeatA1 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'.
#' @param repeatA2 The number of repeats N is used during resampling
#' prediction. The default is 1 for 'CV'.
#' @param repeatB1 The number of repeats N is used for generating stage-2 test
#' data prediction scores. The default is 20.
#' @param repeatB2 The number of repeats N is used for test data prediction.
#' The default is 1.
#' @param nfolds The number of folds is defined for cross validation.
#' The default is 10.
#' @param FSmethod1 Feature selection methods at stage-1. Available options
#' are c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test',
#' 'posWilcox').
#' @param FSmethod2 Feature selection methods at stage-2. Features that are
#' positively associated with the outcome will be used.
#' @param cutP1 The cutoff used for p value thresholding at stage-1.
#' Commonly used cutoffs are c(0.5, 0.1, 0.05, 0.01, etc). If "FSmethod1" is NULL,
#' Then no cutoff is applied.
#' If FSmethod = "posTopCor", cutP is defined as the number of most
#' correlated features with 'fdr' = NULL.
#' @param cutP2 The cutoff used for p value thresholding at stage-2.
#' Commonly used cutoffs are c(0.5, 0.1, 0.05, 0.01, etc). If "FSmethod2" is NULL,
#' Then no cutoff is applied.
#' If FSmethod = "posTopCor", cutP is defined as the number of most
#' correlated features with 'fdr' = NULL.
#' @param fdr2 Multiple testing correction method at stage-2.
#' Available options are c(NULL, 'fdr', 'BH', 'holm', etc).
#' See also \code{\link[stats]{p.adjust}}. The default is NULL.
#' This option is useful particularly when large sets of pathways are investigated.
#' @param FScore The number of cores used for feature selection.
#' @param classifier Machine learning classifiers at both stages.
#' Available options are c('randForest', 'SVM', 'glmnet').
#' @param predMode The prediction mode at both stages. Available options are
#' c('probability', 'classification', 'regression').
#' @param paramlist A list of model parameters at both stages. The set of parameters
#' are different for each classifier. Please see the detailed parameters are
#' implemented for each individual classifier, e.g., 'baseRandForest()', 'baseSVM()',
#' and 'baseGLMnet()'.
#' @param innerCore The number of cores used for computation. It needs to be reconciled
#' with "FScore" depending on the number of cores available.
#' @return The CV or BS predicted score for the training data and
#' test set predicted score if \code{testData} is given.
#' @details Stage-2 training data can be learned either using bootstrapping
#' or cross validation resampling methods in the supervised learning settting.
#' Stage-2 test data is learned via independent test set prediction.
#' @export
#' @references Chen, J., & Schwarz, E. (2017). BioMM: Biologically-informed
#' Multi-stage Machine learning for identification of epigenetic fingerprints.
#' arXiv preprint arXiv:1712.00336.
#' @references Perlich, C., & Swirszcz, G. (2011). On cross-validation and
#' stacking: Building seemingly predictive models on random data. ACM SIGKDD
#' Explorations Newsletter, 12(2), 11-15.
#' @seealso \code{\link{reconBySupervised}}; \code{\link{reconByUnsupervised}};
#' \code{\link{BioMMstage2pred}}
#' @examples
#' ## Load data
#' methylfile <- system.file('extdata', 'methylData.rds', package='BioMM')
#' methylData <- readRDS(methylfile)
#' testData <- NULL
#' ## Annotation file
#' probeAnnoFile <- system.file('extdata', 'cpgAnno.rds', package='BioMM')
#' probeAnno <- readRDS(file=probeAnnoFile)
#' golist <- readRDS(system.file("extdata", "goDB.rds", package="BioMM"))
#' pathlistDB <- golist[1:100]
#' supervisedStage1=TRUE
#' classifier <- 'randForest'
#' predMode <- 'classification'
#' paramlist <- list(ntree=300, nthreads=30)
#' library(BiocParallel)
#' library(ranger)
#' param1 <- MulticoreParam(workers = 2)
#' param2 <- MulticoreParam(workers = 20)
#' ## Not Run
#' ## result <- BioMM(trainData=methylData, testData=NULL,
#' ## pathlistDB, featureAnno=probeAnno,
#' ## restrictUp=200, restrictDown=10, minPathSize=10,
#' ## supervisedStage1, typePCA='regular',
#' ## resample1='BS', resample2='CV', dataMode="allTrain",
#' ## repeatA1=20, repeatA2=1, repeatB1=20, repeatB2=1,
#' ## nfolds=10, FSmethod1=NULL, FSmethod2=NULL,
#' ## cutP1=0.1, cutP2=0.1, fdr2=NULL, FScore=param1,
#' ## classifier, predMode, paramlist, innerCore=param2)
#' ## if (is.null(testData)) {
#' ## predY <- result
#' ## trainDataY <- methylData[,1]
#' ## metricCV <- getMetrics(dataY = trainDataY, predY)
#' ## message("Cross-validation prediction performance:")
#' ## print(metricCV)
#' ## } else if (!is.null(testData)){
#' ## trainDataY <- methylData[,1]
#' ## testDataY <- testData[,1]
#' ## cvYscore <- result[[1]]
#' ## testYscore <- result[[2]]
#' ## metricCV <- getMetrics(dataY = trainDataY, cvYscore)
#' ## metricTest <- getMetrics(dataY = testDataY, testYscore)
#' ## message("Cross-validation performance:")
#' ## print(metricCV)
#' ## message("Test set prediction performance:")
#' ## print(metricTest)
#' ## }
BioMM <- function(trainData, testData, pathlistDB, featureAnno,
restrictUp, restrictDown, minPathSize, supervisedStage1 = TRUE,
typePCA, resample1 = "BS", dataMode = "allTrain",
repeatA1 = 100, repeatA2 = 1, repeatB1 = 20, repeatB2 = 1,
nfolds = 10, FSmethod1, FSmethod2,
cutP1, cutP2, fdr2, FScore = MulticoreParam(), classifier,
predMode, paramlist, innerCore = MulticoreParam()) {
trainDataList <- omics2pathlist(data=trainData, pathlistDB,
featureAnno, restrictUp,
restrictDown, minPathSize)
testDataList <- omics2pathlist(data=testData, pathlistDB,
featureAnno, restrictUp,
restrictDown, minPathSize)
## generation of stage-2 data
if (supervisedStage1 == TRUE) {
stage2data <- reconBySupervised(trainDataList = trainDataList,
testDataList = testDataList, resample = resample1, dataMode,
repeatA = repeatA1, repeatB = repeatB1, nfolds,
FSmethod = FSmethod1, cutP = cutP1, fdr = NULL, FScore = FScore,
classifier = classifier, predMode = predMode,
paramlist = paramlist, innerCore = innerCore,
outFileA = NULL, outFileB = NULL)
} else {
stage2data <- reconByUnsupervised(trainDataList = trainDataList,
testDataList = testDataList, typeMode = typePCA, topPC = 1,
innerCore = innerCore, outFileA = NULL, outFileB = NULL)
}
## if testData provided
trainData2 <- stage2data[[1]]
testData2 <- stage2data[[2]]
datalist <- getDataByFilter(trainData2, testData2, FSmethod = "positive",
cutP = 0.1, fdr = NULL, FScore = FScore)
## include the label
trainPos2 <- datalist[[1]]
message(paste0("Number of positive blocks: ", ncol(trainPos2) - 1))
## If no positive features
if (is.null(trainPos2)) {
message("Warning: No positive/useful stage-2 features!!")
result <- data.frame(AUC = 0.5, ACC = 0.5, R2 = 0)
} else if (!is.null(trainPos2)){
## make prediction
result <- BioMMstage2pred(trainData = trainData2, testData = testData2,
dataMode, repeatA = repeatA2,
repeatB = repeatB2, nfolds, FSmethod = FSmethod2, cutP = cutP2,
fdr = fdr2, FScore = FScore, classifier = classifier,
predMode = predMode, paramlist = paramlist, innerCore = innerCore)
}
return(result)
}
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