SCFA.class: SCFA.class

In duct317/SCFA: SCFA: Subtyping via Consensus Factor Analysis

SCFA.class

Description

Perform risk score prediction on input data. This function requires training data with survival information. The output is the risk scores of patients in testing set.

Usage

SCFA.class(dataListTrain, trainLabel, dataListTest, ncores = 10L, seed = NULL)

Arguments

dataListTrain	List of training data matrices. In each matrix, rows represent samples and columns represent genes/features.
trainLabel	Survival information of patient in training set in form of Surv object.
dataListTest	List of testing data matrices. In each matrix, rows represent samples and columns represent genes/features.
ncores	Number of processor cores to use.
seed	Seed for reproducibility, you still need to use set.seed function for full reproducibility.

Value

A vector of risk score predictions for patient in test set.

Examples

#Load example data (GBM dataset)
data("GBM")
#List of one matrix (microRNA data)
dataList <- GBM$data
#Survival information
survival <- GBM$survival
library(survival)
#Split data to train and test
set.seed(1)
idx <- sample.int(nrow(dataList[[1]]), round(nrow(dataList[[1]])/2) )
survival$Survival <- survival$Survival - min(survival$Survival) + 1 # Survival time must be positive
trainList <- lapply(dataList, function(x) x[idx, ] )
trainSurvival <- Surv(time = survival[idx,]$Survival, event =  survival[idx,]$Death)
testList <- lapply(dataList, function(x) x[-idx, ] )
testSurvival <- Surv(time = survival[-idx,]$Survival, event =  survival[-idx,]$Death)
#Perform risk prediction
result <- SCFA.class(trainList, trainSurvival, testList, seed = 1, ncores = 2L)
#Validation using concordance index
c.index <- concordance(coxph(testSurvival ~ result))$concordance
print(c.index)

duct317/SCFA documentation built on April 18, 2023, 10:42 a.m.