View source: R/get.consensus.subtypes.R
get.consensus.subtypes | R Documentation |
Get consensusOV ovarian cancer subtypes
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)
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
|
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
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.
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)
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