generateGED: A function to simulate two groups gene expression data (GED)
In SPreFuGED: Selecting a Predictive Function for a Given Gene Expression Data

Description Usage Arguments Value Author(s) References See Also Examples

This function draws two sets of vectors (train and test samples' labels) from a binomial distribution and generates two gene expression datasets (train and test data) from a multivariate normal distribution with a mean vector U[6,10] and a given within-class covariance matrix, at each iteration.

1	generateGED(covAll, nTrain, nTest, log2FC = 1, niter = 3, prob = 0.5)

`covAll`	an object returned by covMax or a list containing a covariance matrix cov and the proportion of DE genes pie.
`nTrain`	the number of samples in the training set
`nTest`	the number of samples in the test set
`log2FC`	the absolute Log2 fold changes (effect sizes) for DE genes (Default is 1)
`niter`	the number of iterations (train/test datasets to be generated). Default is 3
`prob`	the probability of success for the binomial sampling. Default is 0.5

A list of length niter. Each element of which is a list containing:

`trainData`	a matrix of the training data
`trainLabels`	a binary vector of class labels of the training samples
`testData`	a matrix of the test data
`testLabels`	a binary vector of class labels of the test samples

Victor Lih Jong

Jong VL, Novianti PW, Roes KCB & Eijkemans MJC. Selecting a classification function for class prediction with gene expression data. Bioinformatics (2016) 32(12): 1814-1822

covMat, directClass and plotDirectClass

myCov<-covMat(pAll=100, lambda=2, corrDE=0.75, sigma=0.25);
myData<-generateGED(covAll=myCov, nTrain=30, nTest=10);
myFirstTrainData<-myData[[1]]$trainData; myFirstTrainLabels<-myData[[1]]$trainLabels;
myFirstTestData<-myData[[1]]$testData; myFirstTestLabels<-myData[[1]]$testLabels;