RTopper (version 1.18.0)

computeDrStat: Computes gene-to-phenotype associations scores

Description

computeDrStat Computes gene-to-phenotype associations scores, using as input the output from convertToDr.

Usage

computeDrStat(data, columns = c(1:(ncol(data)-1)), method = "dev", integrate = TRUE)

Arguments

data

a list of data.frames containing genomic measurements. Each element of dataIntersection must account for the same set of patients(columns) and genes (rows)

columns

a data.frame indicating patients' phenotypic class

method

character, the number of genomic platforms

integrate

logical, wheter to integrate the gene-to-phenotype scores across platform or return separates scores for each platform

Value

A list of named vectors containing separate or integrared gene-to-phenotype association scores.

Details

This function allows computing gene-to-phenotype association scores, using as input the gene-centered list produced by computeDr. The computeDrStat function works separately on each gene-centered data.frame created by the convertToDr function, assuming that the phenotype information is stored in the last column named "response". It is possible computing both separate association scores for each platform, as well as an integrated score, as specified by the integrate arguments. There are currently three methods available for obtaining the scores (see Tyekucheva et al, manuscript under review), as specified by the methods argument: "dev": this approach computes the score as the difference of deviances; "aic": this approach computes the score as the Akaike information criterion for model selection; "bic": this approach computes the score as the penalized likelihood ratio;

References

Svitlana Tyekucheva, Luigi Marchionni, Rachel Karchin, and Giovanni Parmigiani. "Integrating diverse genomic data using gene sets." Manuscript submitted.

Examples

###load data
data(exampleData)

###convert
dataDr <- convertToDr(dat, pheno, 4)

###compute the integrated score
bicStatIntegrated <- computeDrStat(dataDr, columns = c(1:4), method="bic", integrate = TRUE)

###compute separate scores for each genomic platform
bicStatSeparate <- computeDrStat(dataDr, columns = c(1:4), method="bic", integrate = FALSE)