Gen.Alg.Consensus function

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.

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

## 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)