In bhklab/consensusOV: Gene expression-based subtype classification for high-grade serous ovarian cancer

knitr::opts_chunk$set(echo = TRUE, cache=TRUE)

Introduction

r Biocpkg("consensusOV") is a package for molecular subtyping for ovarian cancer. It is intended for whole-transcriptome gene expression datasets from patients with high-grade serous ovarian carcinoma. This package includes implementations of four previously published subtype classifiers (Helland et al., 2011; Bentink et al., 2012; Verhaak et al., 2013; Konecny et al., 2014) and a consensus random forest classifier (Chen et al., 2018).

The get.subtypes() function is a wrapper for the other package subtyping functions get.consensus.subtypes(), get.konecny.subtypes(), get.verhaak.subtypes(), get.bentink.subtypes(), get.helland.subtypes(). It can take as input either a matrix of gene expression values and a vector of Entrez IDs; or an ExpressionSet from the r Biocpkg("Biobase") package. If expression.dataset is a matrix, it should be formatted with genes as rows and patients as columns; and entrez.ids should be a vector with length the same as nrow(expression.dataset). The method argument specifies which of the five subtype classifiers to use.

Load Data

library(consensusOV)
library(Biobase)
library(genefu)

The package contains a subset of the ovarian cancer microarray dataset GSE14764 as example data.

data(GSE14764.eset)
dim(GSE14764.eset)
GSE14764.expression.matrix <- exprs(GSE14764.eset)
GSE14764.expression.matrix[1:5,1:5]
GSE14764.entrez.ids <- fData(GSE14764.eset)$EntrezGene.ID
head(GSE14764.entrez.ids)

Subtyping

bentink.subtypes <- get.subtypes(GSE14764.eset, method = "Bentink")
bentink.subtypes$Bentink.subtypes
konecny.subtypes <- get.subtypes(GSE14764.eset, method = "Konecny")
konecny.subtypes$Konecny.subtypes
helland.subtypes <- get.subtypes(GSE14764.eset, method = "Helland")
helland.subtypes$Helland.subtypes

# to align with the Verhaak subtypes, we need to remove the "geneid." in the rownames
temp_eset <- GSE14764.eset
rownames(temp_eset) <- gsub("geneid.", "", rownames(temp_eset))

verhaak.subtypes <- get.subtypes(temp_eset, method = "Verhaak")

verhaak.subtypes$Verhaak.subtypes
consensus.subtypes <- get.subtypes(GSE14764.eset, method = "consensusOV")
consensus.subtypes$consensusOV.subtypes

## Alternatively, e.g.
data(sigOvcAngiogenic)
bentink.subtypes <- get.subtypes(GSE14764.expression.matrix, GSE14764.entrez.ids, method = "Bentink")

Each subtyping function outputs a list with two values. The first value is a factor of subtype labels. The second is an classifier-specific values. For the Konecny, Helland, Verhaak, and Consensus classifiers, this object is a dataframe with subtype specific scores. For the Bentink classifier, the object is the output of the genefu function call.

Subtype classifiers can alternatively be called using inner functions.

bentink.subtypes <- get.bentink.subtypes(GSE14764.expression.matrix, GSE14764.entrez.ids)

Subtype Scores

The Konecny, Helland, Verhaak, and Consensus classifiers produce real-valued subtype scores. We can use these in various ways - for example, here, we compute correlations between correspinding subtype scores.

We can compare the subtype scores between the Verhaak and Helland classifiers:

vest <- verhaak.subtypes$gsva.out
vest <- vest[,c("IMR", "DIF", "PRO", "MES")]
hest <- helland.subtypes$subtype.scores
hest <- hest[, c("C2", "C4", "C5", "C1")]
dat <- data.frame(
    as.vector(vest), 
    rep(colnames(vest), each=nrow(vest)),
    as.vector(hest), 
    rep(colnames(hest), each=nrow(hest)))
colnames(dat) <- c("Verhaak", "vsc", "Helland", "hsc")
## plot
library(ggplot2)
ggplot(dat, aes(Verhaak, Helland)) + geom_point() + facet_wrap(vsc~hsc, nrow = 2, ncol = 2)

Corresponding correlation values are r round(cor(verhaak.subtypes$gsva.out[,"DIF"], helland.subtypes$subtype.scores[,"C4"]), digits=2), r round(cor(verhaak.subtypes$gsva.out[,"IMR"], helland.subtypes$subtype.scores[,"C2"]), digits=2), r round(cor(verhaak.subtypes$gsva.out[,"MES"], helland.subtypes$subtype.scores[,"C1"]), digits=2), and r round(cor(verhaak.subtypes$gsva.out[,"PRO"], helland.subtypes$subtype.scores[,"C5"]), digits=2).

Likewise, we can compare the subtype scores between the Konecny and Helland classifier:

kost <- konecny.subtypes$spearman.cc.vals
hest <- helland.subtypes$subtype.scores
hest <- hest[, c("C2", "C4", "C5", "C1")]
dat <- data.frame(
    as.vector(kost), 
    rep(colnames(kost), each=nrow(kost)),
    as.vector(hest), 
    rep(colnames(hest), each=nrow(hest)))
colnames(dat) <- c("Konecny", "ksc", "Helland", "hsc")
## plot
ggplot(dat, aes(Konecny, Helland)) + geom_point() + facet_wrap(ksc~hsc, nrow = 2, ncol = 2)

Corresponding correlation values are r round(cor(konecny.subtypes$spearman.cc.vals[,"C1_immL"], helland.subtypes$subtype.scores[,"C2"]), digits=2), r round(cor(konecny.subtypes$spearman.cc.vals[,"C2_diffL"], helland.subtypes$subtype.scores[,"C4"]), digits=2), r round(cor(konecny.subtypes$spearman.cc.vals[,"C3_profL"], helland.subtypes$subtype.scores[,"C5"]), digits=2), and r round(cor(konecny.subtypes$spearman.cc.vals[,"C4_mescL"], helland.subtypes$subtype.scores[,"C1"]), digits=2).

bhklab/consensusOV documentation built on Feb. 13, 2024, 4:50 a.m.