Gen.Alg.Consensus {HMP}

Find Taxa Separating Two Groups using Multiple Genetic Algorithm's (GA) Consensus

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

	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

	## 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)