80 lines
3.1 KiB
R
80 lines
3.1 KiB
R
#' @title Clustering of individuals
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#' @param data data file
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#' @param format Data format. Format supported: "FASTA", "VCF" ,"BAM", "GenePop"
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#' @param partitionCompare a list of partitions to compare
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#' @param ninds number of individuals
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#' @param rowsFromInd a list of rows for each individual
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#' @param noalle number of alleles
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#' @param adjprior ajuster prior probabilities
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#' @param npops number of populations
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#' @param priorTerm prior terms
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#' @param counts counts
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#' @param sumcounts sumcounts
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#' @param max_iter maximum number of iterations
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#' @param alleleCodes allele codes
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#' @param inp input file
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#' @param popnames population names
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#' @param fixedK if \code{TRUE}, the number of populations is fixed
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#' @param verbose if \code{TRUE}, prints extra output information
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#' @importFrom utils read.delim
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#' @importFrom vcfR read.vcfR
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#' @importFrom Rsamtools scanBam
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#' @importFrom adegenet read.genepop .readExt
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#' @references Samtools: a suite of programs for interacting
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#' with high-throughput sequencing data. <http://www.htslib.org/>
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#' @export
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#' @examples
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#' data <- system.file("extdata", "FASTA_clustering_haploid.fasta", package = "rBAPS")
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#' greedyMix(data, "fasta")
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greedyMix <- function(
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data, format, partitionCompare = NULL, ninds = NULL, rowsFromInd = NULL,
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noalle = NULL, adjprior = NULL, npops = 1L, priorTerm = NULL, counts = NULL,
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sumcounts = NULL, max_iter = 100L, alleleCodes = NULL, inp = NULL,
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popnames = NULL, fixedK = FALSE, verbose = FALSE
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) {
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# Importing and handling data ================================================
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data <- importFile(data, format, verbose)
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c <- list(
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# TODO: get elements from handleData()?
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noalle = noalle,
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rows = NA,
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data = data,
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adjprior = adjprior,
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priorTerm = priorTerm,
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rowsFromInd = rowsFromInd
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)
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# Comparing partitions =======================================================
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if (!is.null(partitionCompare)) {
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logmls <- comparePartitions(
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data, nrow(data), partitionCompare[["partitions"]], ninds, rowsFromInd,
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noalle, adjprior
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)
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}
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# Generating partition summary ===============================================
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logml_npops_partitionSummary <- indMixWrapper(c, npops, counts, sumcounts, max_iter, fixedK, verbose);
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logml <- logml_npops_partitionSummary[["logml"]]
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npops <- logml_npops_partitionSummary[["npops"]]
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partitionSummary <- logml_npops_partitionSummary[["partitionSummary"]]
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# Generating output object ===================================================
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out <- list(
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"alleleCodes" = alleleCodes, "adjprior" = adjprior, "popnames" = popnames,
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"rowsFromInd" = rowsFromInd, "data" = data, "npops" = npops,
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"noalle" = noalle, "mixtureType" = "mix", "logml" = logml
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)
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if (logml == 1) {
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return(out)
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}
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# Writing mixture info =======================================================
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changesInLogml <- writeMixtureInfo(
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logml, rowsFromInd, data, adjprior, priorTerm, NULL, inp, partitionSummary,
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popnames, fixedK
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)
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# Updateing results ==========================================================
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return(c(out, "changesInLogml" = changesInLogml))
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}
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