classify: classify - Classify a BLCA sample from gene expression data

In cit-bioinfo/BLCAsubtyping: Predicting bladder cancer molecular subtypes for different classification systems based on expression profiles

classify - Classify a BLCA sample from gene expression data

Description

This function assigns molecular subtypes to bladder samples using expression data, according to several classification systems.

Usage

classify(expMat, gpl = NULL, symbol = "Gene.Symbol",
  classification.systems = c("Baylor", "UNC", "CIT", "Lund", "MDA",
  "TCGA"))

Arguments

expMat	Matrix or dataframe of normalized expression data. Samples are in column and probesets are in row. Rownames and colnames are required.
gpl	Dataframe with matching information between expMat rownames and HGNC gene symbols. The dataframe must have rownames corresponding to ExpMat rownames, and at least one column with associated gene symbols
symbol	Character string referring to the column name of gpl containing gene symbols
classification.systems	A character vector with names of the classification systems to consider among Baylor, UNC, CIT, Lund, MDA and TCGA.

Value

A dataframe of samples annotated according to each classification system.

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)

Aurelien de Reynies

Examples

data(cit)

#-- Classify using all classification types
res <- classify(cit)
head(res)

#-- Get TCGA classification
tcga_res <- res[, c("ID", "TCGA.subtype")]
head(tcga_res)

cit-bioinfo/BLCAsubtyping documentation built on Dec. 31, 2022, 2:22 p.m.