SCFA.class {SCFA} | R Documentation

## 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)

[Package SCFA version 1.4.0 Index]