# Find Taxa Separating Two Groups using Multiple Genetic Algorithm's (GA) Consensus 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. ```r 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 ## Returns A list containing - **solutions**: The best solution from each run. - **consSol**: The consensus solution. - **selectedIndex**: The selected taxa by column number. ## 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. ## Examples ```r ## 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) ```

Gen.Alg.Consensus function