# Gen.Alg.Consensus: Find Taxa Separating Two Groups using Multiple Genetic... In HMP: Hypothesis Testing and Power Calculations for Comparing Metagenomic Samples from HMP

## Description

GA-Mantel is a fully multivariate method that uses a genetic algorithm to search over possible taxa subsets using the Mantel correlation as the scoring measure for assessing the quality of any given taxa subset.

## Usage

 ```1 2``` ``` Gen.Alg.Consensus(data, covars, consensus = .5, numRuns = 10, parallel = FALSE, cores = 3, ...) ```

## Arguments

 `data` A matrix of taxonomic counts(columns) for each sample(rows). `covars` A matrix of covariates(columns) for each sample(rows). `consensus` The required fraction (0, 1] of solutions containing an edge in order to keep it. `numRuns` Number of runs to do. In practice the number of runs needed varies based on data set size and the GA parameters set. `parallel` When this is 'TRUE' it allows for parallel calculation of the bootstraps. Requires the package `doParallel`. `cores` The number of parallel processes to run if parallel is 'TRUE'. `...` Other arguments for the GA function see Gen.Alg

## Details

Use a GA consensus approach to find taxa that separate subjects based on group membership or set of covariates if you cannot run the GA long enough to get a final solution.

## Value

A list containing

 `solutions` The best solution from each run. `consSol` The consensus solution. `selectedIndex` The selected taxa by column number.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29``` ``` ## Not run: data(saliva) data(throat) ### Combine the data into a single data frame group.data <- list(saliva, throat) group.data <- formatDataSets(group.data) data <- do.call("rbind", group.data) ### Normalize the data by subject dataNorm <- t(apply(data, 1, function(x){x/sum(x)})) ### Set covars to just be group membership memb <- c(rep(0, nrow(saliva)), rep(1, nrow(throat))) covars <- matrix(memb, length(memb), 1) ### We use low numbers for speed. The exact numbers to use depend ### on the data being used, but generally the higher iters and popSize ### the longer it will take to run. earlyStop is then used to stop the ### run early if the results aren't improving. iters <- 500 popSize <- 200 earlyStop <- 250 numRuns <- 3 gaRes <- Gen.Alg.Consensus(dataNorm, covars, .5, numRuns, FALSE, 3, iters, popSize, earlyStop) ## End(Not run) ```

HMP documentation built on Aug. 31, 2019, 5:05 p.m.