non_dominated_summary {GSgalgoR} | R Documentation

## Summary of the non dominated solutions

### 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"
    )

[Package GSgalgoR version 1.2.1 Index]