classify_multiple {GSgalgoR} | R Documentation |
Classify samples from multiple centroids
classify_multiple(prob_matrix, centroid_list, distancetype = "pearson")
prob_matrix |
a |
centroid_list |
a |
distancetype |
a |
Returns a data.frame
with the classes assigned to
each sample in each signature, were samples are a rows and signatures
in columns
# 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)