classify_multiple {GSgalgoR} | R Documentation

## Classify samples from multiple centroids

### 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` | a`list` 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)

[Package GSgalgoR version 1.2.1 Index]