Package 'consensusOV'

Title: Gene expression-based subtype classification for high-grade serous ovarian cancer
Description: This package implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype.
Authors: Gregory M Chen [aut], Lavanya Kannan [aut], Ludwig Geistlinger [aut], Victor Kofia [aut], Levi Waldron [aut], Christopher Eeles [ctb], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <[email protected]>
License: Artistic-2.0
Version: 1.27.0
Built: 2024-07-01 05:20:03 UTC
Source: https://github.com/bioc/consensusOV

Help Index

Merging all individual esets and merging them into a big eset

Description

Merging all individual esets and merging them into a big eset

Usage

dataset.merging(
  esets,
  method = c("union", "intersect"),
  standardization = c("quantile", "robust.scaling", "scaling", "none"),
  nthread = 1
)

Arguments

esets

The list containing all GSE file that need to be merged.
method

either "unique" or "intersect" is use to for selecting geneid
standardization

choose between "quantile", "robust.scaling", "scaling" or "none"
nthread

number of threads (1 by default)

Value

The merging eset

Get ovarian cancer subtypes as defined by Bentink et al., 2012

Description

Get ovarian cancer subtypes as defined by Bentink et al., 2012

Usage

get.bentink.subtypes(expression.matrix, entrez.ids)

Arguments

expression.matrix

A matrix of gene expression values with rows as genes, columns as samples.
entrez.ids

A vector of Entrez Gene IDs, corresponding to the rows of expression.matrix

Value

A list with first value Bentink.subtypes containing a factor of subtype names; and second value angio containing the ouput of genefu::ovcAngiogenic

References

Bentink et al. Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer. PloS one (2012).

Examples

library(Biobase)
library(genefu)
data(GSE14764.eset)
expression.matrix <- exprs(GSE14764.eset)
entrez.ids <- as.character(fData(GSE14764.eset)$EntrezGene.ID)
get.bentink.subtypes(expression.matrix, entrez.ids)

Get consensusOV ovarian cancer subtypes

Description

Get consensusOV ovarian cancer subtypes

Usage

get.consensus.subtypes(
  expression.matrix,
  entrez.ids,
  concordant.tumors.only = TRUE,
  remove.using.cutoff = FALSE,
  percentage.dataset.removed = 0.75,
  .training.dataset = consensus.training.dataset.full,
  .dataset.names.to.keep = names(esets.rescaled.classified.filteredgenes)
)

margin(rf.probs)

Arguments

expression.matrix

A matrix of gene expression values with rows as genes, columns as samples.
entrez.ids

A vector of Entrez Gene IDs, corresponding to the rows of expression.matrix
concordant.tumors.only

Logical. Should the classifier trained only on tumors that are concordantly classified by Helland, Konecny, and Verhaak? Defaults to TRUE.
remove.using.cutoff

Specify whether to classify NA for samples that do not meet a margin cutoff
percentage.dataset.removed

If remove.using.cutoff is TRUE, then classify this percentage of samples to NA based on margin values
.training.dataset

ExpressionSet containing the training data. Defaults to the pooled dataset across selected MetaGxOvarian datasets.
.dataset.names.to.keep

Names of MetaGxOvarian datasets to use for training
rf.probs

random forest probabilities for each subtype as returned by get.consensus.subtypes

Value

get.consensus.subtypes returns a list with first value consensusOV.subtypes containing a factor of subtype labels; and second value rf.probs containing a matrix of subtype probabilities.

margin returns a numeric vector containing the classification margin scores, i.e. the difference between the top two subtype scores for each tumor.

Examples

library(Biobase)
data(GSE14764.eset)
expression.matrix <- exprs(GSE14764.eset)
entrez.ids <- as.character(fData(GSE14764.eset)$EntrezGene.ID)
sts <- get.consensus.subtypes(expression.matrix, entrez.ids)
margins <- margin(sts$rf.probs)

Get ovarian cancer subtypes as defined by Hao et al., 2017

Description

Get ovarian cancer subtypes as defined by Hao et al., 2017

Usage

get.hao.subtypes(expression.matrix, entrez.ids)

Arguments

expression.matrix

A matrix of gene expression values with genes as rows, samples as columns.
entrez.ids

A vector of Entrez Gene IDs, corresponding to the rows of expression.matrix.

Details

Hao et al., 2017 derived a gene signature to predict the tissue of origin of ovarian tumors as either fallopian tube (FT) or ovarian surface epithelium (OSE).

The authors found that expression patterns of tissue-specific genes, prognostic genes, and molecular markers support a dualistic tissue origin of ovarian cancer, from either FT or OSE.

The subtype classifier considers 112 signature genes including 37 genes upregulated in FT and 75 genes upregulated in OSE. A score is computed that is designed to range from 0 to 1 for FT tumors, while OSE tumors have a score ranging from -1 to 0.

Value

A list with first value tissue containing a factor of subtype names (tissue of origin); and second value score containing the tissue-of-origin score.

Author(s)

Ludwig Geistlinger

References

Hao et al. (2017) Integrated analysis reveals tubal- and ovarian-originated serous ovarian cancer and predicts differential therapeutic responses. Clinical Cancer Research, 23:7400-11.

Examples

library(Biobase)
data(GSE14764.eset)
expression.matrix <- exprs(GSE14764.eset)
entrez.ids <- as.character(fData(GSE14764.eset)$EntrezGene.ID)
get.hao.subtypes(expression.matrix, entrez.ids)

Get ovarian cancer subtypes as defined by Helland et al., 2011

Description

Get ovarian cancer subtypes as defined by Helland et al., 2011

Usage

get.helland.subtypes(expression.matrix, entrez.ids)

Arguments

expression.matrix

A matrix of gene expression values with rows as genes, columns as samples.
entrez.ids

A vector of Entrez Gene IDs, corresponding to the rows of expression.matrix

Value

A list with first value Helland.subtypes containing a factor of subtype names; and second value subtype.scores containing a matrix of subtype scores

References

Helland et al. Deregulation of MYCN, LIN28B and LET7 in a molecular subtype of aggressive high-grade serous ovarian cancers. PloS one (2011).

Examples

library(Biobase)
data(GSE14764.eset)
expression.matrix <- exprs(GSE14764.eset)
entrez.ids <- as.character(fData(GSE14764.eset)$EntrezGene.ID)
get.helland.subtypes(expression.matrix, entrez.ids)

Get ovarian cancer subtypes as defined by Konecny et al., 2014

Description

Get ovarian cancer subtypes as defined by Konecny et al., 2014

Usage

get.konecny.subtypes(expression.matrix, entrez.ids)

Arguments

expression.matrix

A matrix of gene expression values with rows as genes, columns as samples.
entrez.ids

A vector of Entrez Gene IDs, corresponding to the rows of expression.matrix

Value

A list with first value Konecny.subtypes containing a factor of subtype names; and second value spearman.cc.vals containing the Spearman correlation values per subtype

References

Konecny et al. Prognostic and therapeutic relevance of molecular subtypes in high-grade serous ovarian cancer. Journal of the National Cancer Institute (2014).

Examples

library(Biobase)
data(GSE14764.eset)
expression.matrix <- exprs(GSE14764.eset)
entrez.ids <- as.character(fData(GSE14764.eset)$EntrezGene.ID)
get.konecny.subtypes(expression.matrix, entrez.ids)

Get ovarian cancer subtypes

Description

Get ovarian cancer subtypes

Usage

get.subtypes(
  expression.dataset,
  entrez.ids = NULL,
  method = c("consensusOV", "Helland", "Verhaak", "Konecny", "Bentink"),
  ...
)

Arguments

expression.dataset

Either a matrix of gene expression values with rows as genes, columns as samples; or a BioBase::ExpressionSet object from MetaGxOvarian. If expression.dataset is a matrix, then entrez.ids must have length equal to the number of rows of expression.dataset.
entrez.ids

A vector of Entrez Gene IDs, corresponding to the rows of expression.dataset
method

The subtyping method to use
...

Optional parameters to be passed to the low level function

Value

A list with first value Konecny.subtypes containing a factor of subtype names; and second value spearman.cc.vals containing the Spearman correlation values per subtype

Examples

library(Biobase)
data(GSE14764.eset)
expression.matrix <- exprs(GSE14764.eset)
entrez.ids <- as.character(fData(GSE14764.eset)$EntrezGene.ID)
get.subtypes(expression.matrix, entrez.ids, method="Konecny")

Get ovarian cancer subtypes as defined by Verhaak et al., 2013

Description

Get ovarian cancer subtypes as defined by Verhaak et al., 2013

Usage

get.verhaak.subtypes(expression.matrix, entrez.ids)

Arguments

expression.matrix

A matrix of gene expression values with rows as genes, columns as samples.
entrez.ids

A vector of Entrez Gene IDs, corresponding to the rows of expression.matrix

Value

A list with first value Verhaak.subtypes containing a factor of subtype names; and second value gsva containing the GSVA subtype scores

References

Verhaak et al. Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. The Journal of Clinical Investigation (2013)

Examples

library(Biobase)
data(GSE14764.eset)
expression.matrix <- exprs(GSE14764.eset)
entrez.ids <- as.character(fData(GSE14764.eset)$EntrezGene.ID)
get.konecny.subtypes(expression.matrix, entrez.ids)

Sample ExpressionSet from MetaGxOvarian

Description

A Biobase::ExpressionSet from package MetaGxOvarian for the dataset GSE14764

Usage

GSE14764.eset

Format

A Biobase::ExpressionSet object

Source

http://biorxiv.org/content/biorxiv/early/2016/05/12/052910.full.pdf