fitJM {IntegratedJM}

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.

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.

Value

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

Examples

## Not run: 
jmRes <- fitJM(dat=gene_eset,responseVector=activity,methodMultTest='fdr')
jmRes <- fitJM(dat=gene_eset,responseVector=activity,covariate = fp,methodMultTest='fdr')

## End(Not run)