GARS_FitFun: This function implements the Fitness Function of GARS

In BioinfoMonzino/GARS: GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets

Description

In GARS the Fitness Function consists in calculating the Averaged Silhouette Index after a Multi-Dimensional Scaling

Usage

GARS_FitFun(data, classes, chr.pop)

Arguments

data	A SummarizedExperiment object or a matrix or a data.frame. In case of matrix or data.frame: Rows and Cols have to be, respectively, observations and features. The variables are tipically genes; GARS also accept other -omic features as well as any continuous or factorial variables (e.g. sex, age, cholesterol level,...); Usually the number of observation is << than the number of features '
classes	A vector of type "factor" with nrow(data) elements. Each element represents the class label for each observation.
chr.pop	A matrix or a data.frame representing the chromosomes population: each column is a chromosome and each element corresponds to the feature position in the expression data matrix

Value

A numeric vector where each element corresponds to the fitness score of each chromosome in 'chr.pop'

Author(s)

Mattia Chiesa, Luca Piacentini

See Also

GARS_create_rnd_population

Examples

# use example data:
data(GARS_data_norm)
data(GARS_classes)
data(GARS_popul)
fitness_scores <- GARS_FitFun(GARS_data_norm, GARS_classes, GARS_popul)

BioinfoMonzino/GARS documentation built on Aug. 2, 2021, 1:08 a.m.