topkGenes: topkGenes

In IntegratedJM: Joint Modeling of the Gene-Expression and Bioassay Data, Taking Care of the Effect Due to a Fingerprint Feature

Description

The topkGenes function is to identify the top genes based on different criteria.

Usage

topkGenes(jointModelResult, subset_type, ranking, k = 10, sigLevel = 0.01)

Arguments

jointModelResult	Data frame, containing the results from the fitJM function.
subset_type	Character string to specify the set of genes. It can have four values: "Effect" for only differentially expressed genes, "Correlation" for only correlated genes, "Effect and Correlation" for genes which are both differentially expressed & correlated and "Other" for the genes which are neither differentially expressed nor correlated.
ranking	Character string, specifying one of the columns of the jointModelResult data frame, based on the genes will be ranked within the selected subset.
k	Integer, specifying the number of genes, to be returned from the list of top genes. Default is 10.
sigLevel	Numeric between 0 and 1, specifying the level of significance, used to select the subset of genes.

Details

Returned data frame contains 6 columns, named as "Genes","FP-Effect", "p-adj(Effect)", "Unadj.Asso.","Adj.Asso.", "p-adj(Adj.Asso.)".

Value

A data frame containing top k genes according to the specified criteria from the specified set of genes.

Examples

## Not run: 
topkGenes(jointModelResult=jmRes,subset_type="Effect",ranking="Pearson",k=10,sigLevel = 0.05)

## End(Not run)

IntegratedJM documentation built on May 2, 2019, 6:47 p.m.