non_dominated_summary: Summary of the non dominated solutions

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

Description

The function uses a 'galgo.Obj' as input an the training dataset to evaluate the non-dominated solutions found by GalgoR

Usage

non_dominated_summary (output, prob_matrix, OS,
distancetype = "pearson")

Arguments

output	An object of class galgo.Obj
prob_matrix	a matrix or data.frame. Must be an expression matrix with features in rows and samples in columns
OS	a survival object (see Surv function from the survival package)
distancetype	a character that can be either 'pearson', 'uncentered', 'spearman' or 'euclidean'

Value

Returns a data.frame with 5 columns and a number of rows equals to the non-dominated solutions found by GalgoR. The first column has the name of the non-dominated solutions, the second the number of partitions found for each solution (k), the third, the number of genes, the fourth the mean silhouette coefficient of the solution and the last columns has the estimated C.Index for each one.

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)
non_dominated_summary(
    output = output,
    OS = OS,
    prob_matrix = expression,
    distancetype = "pearson"
)

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