ASSIGN (version 1.8.0)

assign.preprocess: Input data preprocessing

Description

The assign.preprocess function is used to perform quality control on the user-provided input data and generate starting values and/or prior values for the model parameters. The assign.preprocess function is optional. For users who already have the correct format for the input of the assign function, they can skip this step and go directly to the assign.mcmc function.

Usage

assign.preprocess(trainingData=NULL, testData, trainingLabel, geneList=NULL, 
n_sigGene=NA, theta0=0.05, theta1=0.9)

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 details and examples for more information.

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.

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.

Value

trainingData_sub

The G x N matrix of G genomic measures (i.g., gene expession) of N training samples. Genes/probes present in at least one pathway signature are retained. Only returned when the training dataset is available.

testData_sub

The G x N matrix of G genomic measures (i.g., gene expession) of N test samples. Genes/probes present in at least one pathway signature are retained.

B_vector

The G x 1 vector of genomic measures of the baseline/background. Each element of the B_vector is calculated as the mean of the genomic measures of the control samples in training data.

S_matrix

The G x K matrix of genomic measures of the signature. Each column of the S_matrix represents a pathway. Each element of the S_matrix is calculated as the mean of genomic measures of the experimental samples minus the mean of the control samples in the training data.

Delta_matrix

The G x K matrix of binary indicators. Each column of the Delta_matrix represents a pathway. The elements in Delta_matrix are binary (0, insignificant gene; 1, significant gene).

Pi_matrix

The G x K matrix of probability p of a Bernoulli distribution. Each column of the Pi_matrix represents a pathway. Each element in the Pi_matrix is the probability of a gene to be significant in its associated pathway.

diffGeneList

The list that collects the signature genes of one/multiple pathways generated from the training samples or from the user provided gene list. Every component of this list contains the signature genes associated with one pathway.

Details

The assign.preprocess is applied to perform quality control on the user-provided genomic data and meta data, re-format the data in a way that can be used in the following analysis, and generate starting/prior values for the pathway signature matrix. The output values of the assign.preprocess function will be used as input values for the assign.mcmc function.

For training data with 1 control group and 3 experimental groups (10 samples/group; all 3 experimental groups share 1 control group), the trainingLabel can be specified as: trainingLabel <- list(control = list(expr1=1:10, expr2=1:10, expr3=1:10), expr1 = 11:20, expr2 = 21:30, expr3 = 31:40)

For training data with 3 control groups and 3 experimental groups (10 samples/group; Each experimental group has its corresponding control group), the trainingLabel can be specified as: trainingLabel <- list(control = list(expr1=1:10, expr2=21:30, expr3=41:50), expr1 = 11:20, expr2 = 31:40, expr3 = 51:60) It is highly recommended that the user use the same expriment name when specifying control indice and exprimental indice.

Examples

                       
processed.data <- assign.preprocess(trainingData=trainingData1, 
testData=testData1, trainingLabel=trainingLabel1, geneList=geneList1)