ASSIGN (version 1.8.0)

assign.wrapper: ASSIGN All-in-one function

Description

The assign.wrapper function integrates the assign.preprocess, assign.mcmc, assign.summary, assign.output, assign.cv.output functions into one wrapper function.

Usage

assign.wrapper(trainingData=NULL, testData, trainingLabel, testLabel=NULL, 
geneList=NULL, n_sigGene=NA, adaptive_B=TRUE, adaptive_S=FALSE, mixture_beta=TRUE, 
outputDir, p_beta=0.01, theta0=0.05, theta1=0.9, iter=2000, burn_in=1000)

Arguments

trainingData

The genomic measure matrix of training samples (i.g., gene expression matrix). The dimension of this matrix is probe number x sample number. The default is NULL.

testData

The genomic measure matrix of test samples (i.g., gene expression matrix). The dimension of this matrix is probe number x sample number.

trainingLabel

The list linking the index of each training sample to a specific group it belongs to. See examples for more information.

testLabel

The vector of the phenotypes/labels of the test samples. The default is NULL.

geneList

The list that collects the signature genes of one/multiple pathways. Every component of this list contains the signature genes associated with one pathway. The default is NULL.

n_sigGene

The vector of the signature genes to be identified for one pathway. n_sigGene needs to be specified when geneList is set NULL. The default is NA. See examples for more information.

adaptive_B

Logicals. If TRUE, the model adapts the baseline/background (B) of genomic measures for the test samples. The default is TRUE.

adaptive_S

Logicals. If TRUE, the model adapts the signatures (S) of genomic measures for the test samples. The default is FALSE.

mixture_beta

Logicals. If TRUE, elements of the pathway activation matrix are modeled by a spike-and-slab mixuture distribution. The default is TRUE.

outputDir

The path to the directory to save the output files. The path needs to be quoted in double quotation marks.

p_beta

p_beta is the prior probability of a pathway being activated in individual test samples. The default is 0.01.

theta0

The prior probability for a gene to be significant, given that the gene is NOT defined as "significant" in the signature gene lists provided by the user. The default is 0.05.

theta1

The prior probability for a gene to be significant, given that the gene is defined as "significant" in the signature gene lists provided by the user. The default is 0.9.

iter

The number of iterations in the MCMC. The default is 2000.

burn_in

The number of burn-in iterations. These iterations are discarded when computing the posterior means of the model parameters. The default is 1000.

Value

The assign.wrapper returns one/multiple pathway activity for each individual training samples and test samples, scatter plots of pathway activity for each individual pathway in the training and test samples, heatmap plots for gene expression signatures for each individual pathways, heatmap plots for the gene expression of the prior signature and posterior signtures (if adaptive_S equals TRUE) of each individual pathway in the test samples.

Details

The assign.wrapper function is an all-in-one function which output the necessary results for the basic users. For the users who need more intermetiate results for model diagnosis, it is better to run the assign.preprocess, assign.mcmc, assign.convergence, assign.summary functions by order and extract the output values from the returned list objects of those functions.

Examples

data(trainingData1)
data(testData1)
data(geneList1)

trainingLabel1 <- list(control = list(bcat=1:10, e2f3=1:10, myc=1:10, ras=1:10, 
src=1:10), bcat = 11:19, e2f3 = 20:28, myc= 29:38, ras = 39:48, src = 49:55)
testLabel1 <- rep(c("subtypeA","subtypeB"),c(53,58))

assign.wrapper(trainingData=trainingData1, testData=testData1, 
trainingLabel=trainingLabel1, testLabel=testLabel1, geneList=geneList1, 
adaptive_B=TRUE, adaptive_S=FALSE, mixture_beta=TRUE, 
outputDir=tempdir, p_beta=0.01, theta0=0.05, theta1=0.9, 
iter=20, burn_in=10)