classifyCMS.RF: classifyCMS.RF

In Sage-Bionetworks/CMSclassifier: prediction of the Consensus Molecular Subtype (CMS) of colorectal carcinomas based on log2_scaled Gene Expression Profiles (GEP)

Description

Random Forest (RF) predictor of the Consensus Molecular Subtype (CMS) of colorectal cancer samples, based on log2_scaled GEP

Usage

classifyCMS.RF(Exp,center=TRUE,minPosterior=.5)

Arguments

Exp	a dataframe with log2_scaled Gene Expression Profiles (GEP) data values, samples in columns (n>1 mandatory), genes in rows, rownames corresponding to Entrez IDs
center	boolean : should GEP be row-centered (default : TRUE)
minPosterior	numeric : minimal posterior probablity to classify a sample in a CMS

Value

a dataframe, samples in rows, columns = posterior probability to be classified in each of the four CMS centroids, nearest CMS (ie CMS with highest posterior prob), predicted CMS

Note

This is a contribution from the Tumor Identity Cards (CIT) program founded by the 'Ligue Nationale Contre le Cancer' (France): http://cit.ligue-cancer.net. For any question please contact [e-mail]

Author(s)

Justin Guinney, Aurelien de Reynies

Sage-Bionetworks/CMSclassifier documentation built on May 9, 2019, 12:10 p.m.