mogamun_init: mogamun_init

In MOGAMUN: MOGAMUN: A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks

Description

initialize evolution parameters

Usage

mogamun_init(
    Generations = 500,
    PopSize = 100,
    MinSize = 15,
    MaxSize = 50,
    CrossoverRate = 0.8,
    MutationRate = 0.1,
    JaccardSimilarityThreshold = 30,
    TournamentSize = 2,
    Measure = "FDR",
    ThresholdDEG = 0.05,
    MaxNumberOfAttempts = 3
)

Arguments

Generations	number of generations to run (default = 500)
PopSize	number of subnetworks in the population (default = 100)
MinSize	minimum size (no. of nodes) of the subnetworks (default = 15)
MaxSize	maximum size (no. of nodes) of the subnetworks (default = 50)
CrossoverRate	rate for the crossover (default = 0.8)
MutationRate	rate for the mutation (default = 0.1)
JaccardSimilarityThreshold	subnetworks over this Jaccard similarity threshold are considered as duplicated (default = 30)
TournamentSize	size of the tournament (default = 2)
Measure	measure to calculate the nodes scores and to determine which genes are differentially expressed (possible values PValue and FDR, default = FDR)
ThresholdDEG	threshold to consider a gene as significantly differerentially expressed. Note: if there is a logFC available, it is also considered |logFC|>1 (default = 0.05)
MaxNumberOfAttempts	maximum number of attempts to find compatible parents (default = 3)

Value

EvolutionParameters

Examples

EvolutionParameters <- 
    mogamun_init(
        Generations = 1,
        PopSize = 10,
        MinSize = 15,
        MaxSize = 50,
        CrossoverRate = 0.8,
        MutationRate = 0.1,
        JaccardSimilarityThreshold = 30,
        TournamentSize = 2,
        Measure = "FDR",
        ThresholdDEG = 0.05,
        MaxNumberOfAttempts = 3
    )

MOGAMUN documentation built on Feb. 14, 2021, 2 a.m.