galgo: GSgalgoR main function

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

Description

galgo accepts an expression matrix and a survival object to find robust gene expression signatures related to a given outcome

Usage

galgo (population = 30, generations = 2, nCV = 5,
distancetype = "pearson", TournamentSize = 2, period = 1825,
OS, prob_matrix, res_dir = "", start_galgo_callback = callback_default,
end_galgo_callback = callback_base_return_pop,
report_callback = callback_base_report,
start_gen_callback = callback_default,
end_gen_callback = callback_default,
verbose = 2)

Arguments

population	a number indicating the number of solutions in the population of solutions that will be evolved
generations	a number indicating the number of iterations of the galgo algorithm
nCV	number of cross-validation sets
distancetype	character, it can be 'pearson' (centered pearson), 'uncentered' (uncentered pearson), 'spearman' or 'euclidean'
TournamentSize	a number indicating the size of the tournaments for the selection procedure
period	a number indicating the outcome period to evaluate the RMST
OS	a survival object (see Surv function from the survival package)
prob_matrix	a matrix or data.frame. Must be an expression matrix with features in rows and samples in columns
res_dir	a character string indicating where to save the intermediate and final output of the algorithm
start_galgo_callback	optional callback function for the start of the galgo execution
end_galgo_callback	optional callback function for the end of the galgo execution
report_callback	optional callback function
start_gen_callback	optional callback function for the beginning of the run
end_gen_callback	optional callback function for the end of the run
verbose	select the level of information printed during galgo execution

Value

an object of type 'galgo.Obj' that corresponds to a list with the elements $Solutions and $ParetoFront. $Solutions is a l x (n + 5) matrix where n is the number of features evaluated and l is the number of solutions obtained. The submatrix l x n is a binary matrix where each row represents the chromosome of an evolved solution from the solution population, where each feature can be present (1) or absent (0) in the solution. Column n +1 represent the k number of clusters for each solutions. Column n+2 to n+5 shows the SC Fitness and Survival Fitness values, the solution rank, and the crowding distance of the solution in the final pareto front respectively. For easier interpretation of the 'galgo.Obj', the output can be reshaped using the to_list and to_dataframe functions

Author(s)

Martin E Guerrero-Gimenez, [e-mail]

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(seq_len(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)

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