## Introduction

`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 [@konecny2014prognostic; @verhaak2013prognostically; @bentink2012angiogenic; @helland2011deregulation] and a consensus random forest classifier.

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 a BioBase::ExpressionSet following the format of MetaGxOvarian [@gendoo2016metagxdata]. 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)

    ## Features  Samples
    ##     1175       41

    GSE14764.expression.matrix <- exprs(GSE14764.eset)
    GSE14764.expression.matrix[1:5,1:5]

    ##              GSE14764_GSM368661 GSE14764_GSM368662 GSE14764_GSM368664
    ## geneid.10397          10.856712          10.445412          11.976560
    ## geneid.65108          10.856441          10.312760          12.499419
    ## geneid.8655           11.518799          11.897707          11.782895
    ## geneid.22919           8.608944           8.756986           9.170513
    ## geneid.3925            7.658680           6.698586           7.159795
    ##              GSE14764_GSM368665 GSE14764_GSM368668
    ## geneid.10397          11.651318          10.907453
    ## geneid.65108          11.377340          11.088542
    ## geneid.8655           11.799197          11.958500
    ## geneid.22919           8.627511           8.849757
    ## geneid.3925            7.466107           6.566558

    GSE14764.entrez.ids <- fData(GSE14764.eset)$EntrezGene.ID
    head(GSE14764.entrez.ids)

    ## [1] "10397" "65108" "8655"  "22919" "3925"  "1718"

## Subtyping

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

    ##  [1] Angiogenic    nonAngiogenic nonAngiogenic Angiogenic    Angiogenic
    ##  [6] nonAngiogenic Angiogenic    nonAngiogenic nonAngiogenic Angiogenic
    ## [11] nonAngiogenic nonAngiogenic Angiogenic    nonAngiogenic nonAngiogenic
    ## [16] nonAngiogenic nonAngiogenic Angiogenic    nonAngiogenic nonAngiogenic
    ## [21] Angiogenic    Angiogenic    Angiogenic    nonAngiogenic nonAngiogenic
    ## [26] Angiogenic    nonAngiogenic nonAngiogenic nonAngiogenic nonAngiogenic
    ## [31] nonAngiogenic Angiogenic    nonAngiogenic nonAngiogenic nonAngiogenic
    ## [36] nonAngiogenic nonAngiogenic Angiogenic    nonAngiogenic nonAngiogenic
    ## [41] nonAngiogenic
    ## Levels: Angiogenic nonAngiogenic

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

    ##  [1] C3_profL C1_immL  C2_diffL C4_mescL C1_immL  C1_immL  C4_mescL
    ##  [8] C3_profL C3_profL C1_immL  C2_diffL C2_diffL C4_mescL C2_diffL
    ## [15] C3_profL C2_diffL C1_immL  C4_mescL C1_immL  C2_diffL C4_mescL
    ## [22] C4_mescL C4_mescL C1_immL  C3_profL C3_profL C2_diffL C2_diffL
    ## [29] C3_profL C2_diffL C3_profL C1_immL  C1_immL  C2_diffL C1_immL
    ## [36] C2_diffL C3_profL C3_profL C2_diffL C3_profL C2_diffL
    ## Levels: C1_immL C2_diffL C3_profL C4_mescL

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

    ##  [1] C1 C2 C5 C1 C1 C2 C4 C1 C5 C2 C4 C4 C1 C5 C4 C4 C2 C1 C4 C5 C1 C1 C1
    ## [24] C2 C5 C1 C5 C2 C5 C4 C5 C2 C2 C4 C2 C4 C1 C1 C4 C1 C4
    ## Levels: C2 C4 C5 C1

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

    verhaak.subtypes$Verhaak.subtypes

    ##  [1] MES IMR DIF DIF IMR IMR DIF MES PRO IMR DIF DIF MES DIF DIF DIF DIF
    ## [18] MES DIF PRO MES DIF DIF DIF MES MES DIF IMR MES DIF PRO IMR IMR DIF
    ## [35] IMR DIF PRO MES DIF MES DIF
    ## Levels: IMR DIF PRO MES

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

    ## [1] "Loading training data"
    ## [1] "Training Random Forest..."

    consensus.subtypes$consensusOV.subtypes

    ##  [1] MES_consensus IMR_consensus PRO_consensus DIF_consensus IMR_consensus
    ##  [6] IMR_consensus DIF_consensus MES_consensus PRO_consensus IMR_consensus
    ## [11] DIF_consensus DIF_consensus MES_consensus PRO_consensus DIF_consensus
    ## [16] DIF_consensus IMR_consensus MES_consensus DIF_consensus PRO_consensus
    ## [21] MES_consensus DIF_consensus IMR_consensus DIF_consensus PRO_consensus
    ## [26] PRO_consensus DIF_consensus IMR_consensus PRO_consensus PRO_consensus
    ## [31] PRO_consensus IMR_consensus IMR_consensus IMR_consensus IMR_consensus
    ## [36] IMR_consensus PRO_consensus PRO_consensus PRO_consensus MES_consensus
    ## [41] DIF_consensus
    ## Levels: IMR_consensus DIF_consensus PRO_consensus MES_consensus

    ## Alternatively, e.g.
    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 0.77, 0.79, 0.37, and 0.88.

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 0.95, 0.84, 0.7, and 0.95.

## References