IntegratedJM (version 1.6)

fitJM: fitJM

Description

The fitJM function fits the model for all the genes for a specific bio-activity vector and a particular fingerprint feature.

Usage

fitJM(dat, responseVector, covariate = NULL, methodMultTest)

Arguments

dat

Contains the gene expression data matrix for all the genes - can be a matrix or an expression set.

responseVector

Vector containing the bio-activity data.

covariate

Vector of 0's and 1's, containing data about the fingerprint feature.

methodMultTest

Character string to specify the multiple testing method. Default is the BH-FDR method.

Value

A data frame, containing the results of the model, to be used later for plots or to identify the top genes.

Details

The default for the covariate parameter is NULL and if no covariate is specified it returns a data frame containing 5 variables, named as "Pearson","Spearman","p", "adj-p","logratio" and the data frame is ordered based on the column "p" which is the p-value obtained from the Log-Ratio Test. If there is a covariate, then the output is a dataframe containing 13 variables for all the genes,named as "adjPearson","adjSpearman","pPearson","Pearson", "Spearman", "pAdjR", "CovEffect1", "adjPeffect1", "CovEffect2", "adjPeffect2", "rawP1", "rawP2","logratio" and sorted based on "rawP1" and "pPearson" which are p-value corresponding to the effect of the fingerprint feature on the gene expression data as obtained from the t-table after fitting the model using gls and the p-value obtained from the Log-Ratio Test, respectively. In the first case without any covariate it calls the nullcov function inside it, otherwise the non_nullcov function is called to do the analysis.

Examples

# NOT RUN {
jmRes <- fitJM(dat=gene_eset,responseVector=activity,methodMultTest='fdr')
jmRes <- fitJM(dat=gene_eset,responseVector=activity,covariate = fp,methodMultTest='fdr')
# }