classify_multiple: Classify samples from multiple centroids

In GSgalgoR: An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer

Description

Classify samples from multiple centroids

Usage

classify_multiple(prob_matrix, centroid_list, distancetype = "pearson")

Arguments

prob_matrix	a matrix or data.frame. Must be an expression matrix with features in rows and samples in columns
centroid_list	alist with the centroid matrix for each of the signatures to evaluate, where each column represents the prototypic centroid of a subtype and each row the constituents features of the solution signature. The output of create_centroids can be used.
distancetype	a character that can be either 'pearson' (default), 'spearman' or 'kendall'.

Value

Returns a data.frame with the classes assigned to each sample in each signature, were samples are a rows and signatures in columns

Examples

# load example dataset
library(breastCancerTRANSBIG)
data(transbig)
Train <- transbig
rm(transbig)

expression <- Biobase::exprs(Train)
clinical <- Biobase::pData(Train)
OS <- survival::Surv(time = clinical$t.rfs, event = clinical$e.rfs)

# We will use a reduced dataset for the example
expression <- expression[sample(1:nrow(expression), 100), ]

# Now we scale the expression matrix
expression <- t(scale(t(expression)))

# Run galgo
output <- GSgalgoR::galgo(generations = 5, population = 15,
prob_matrix = expression, OS = OS)
outputDF <- to_dataframe(output)
outputList <- to_list(output)

RESULTS <- non_dominated_summary(
    output = output, OS = OS,
    prob_matrix = expression,
    distancetype = "pearson"
)
CentroidsList <- create_centroids(output, RESULTS$solution,
trainset = expression)
classes <- classify_multiple(prob_matrix = expression,
centroid_list = CentroidsList)

GSgalgoR documentation built on Nov. 8, 2020, 6:57 p.m.