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Merge branch 'issue-25' into develop

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* issue-25: (38 commits)
  Adjusted unit tests for #25
  Fixed sink() usage
  Fixed docs
  Exporting importFile()
  Improved handling of supported formats for greedyMix() (#25)
  Fixed basic parsing of FASTA files (#25)
  Increment version number to 0.0.0.9022
  Fixed syntax (#25)
  Improved printing (#25)
  Partial reversion of b034158 (#25)
  Fixed to indMix (#25)
  Incorporated handleData() on greedyMix() (#25)
  Improved handleData() to handle FASTA (#25)
  Added numeric output option to load_fasta() (#25)
  Fixed test text (#25)
  Added missing documentation for arguments (#25)
  Syntax fix (#25)
  Delayed resolution of FIXMEs (#25)
  Workaround for usage of MATLAB any() (#25)
  Fixed argument passing (#25)
  ...
This commit is contained in:
Waldir Leoncio 2023-09-11 13:15:55 +02:00
commit 59fbb0a167
95 changed files with 45170 additions and 45032 deletions
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3
.Rbuildignore
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@ -4,7 +4,6 @@ PITFALLS.md
CHANGELOG.md CHANGELOG.md
CITATION.cff CITATION.cff
.travis.yml .travis.yml
inst/ext/ExamplesDataFormatting inst/testdata
.github .github
aux aux

2
DESCRIPTION
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@ -1,6 +1,6 @@
Package: rBAPS Package: rBAPS
Title: Bayesian Analysis of Population Structure Title: Bayesian Analysis of Population Structure
Version: 0.0.0.9020 Version: 0.0.0.9022
Date: 2020-11-09 Date: 2020-11-09
Authors@R: Authors@R:
c( c(

39
NAMESPACE
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@ -1,45 +1,8 @@
# Generated by roxygen2: do not edit by hand # Generated by roxygen2: do not edit by hand
export(addAlleles)
export(admix1)
export(calculatePopLogml)
export(computeAllFreqs2)
export(computeIndLogml)
export(computePersonalAllFreqs)
export(computeRows)
export(etsiParas)
export(fgetl)
export(fopen)
export(greedyMix) export(greedyMix)
export(greedyPopMix)
export(handleData) export(handleData)
export(handlePopData) export(importFile)
export(initPopNames)
export(learn_partition_modified)
export(learn_simple_partition)
export(linkage)
export(load_fasta)
export(logml2String)
export(lueGenePopData)
export(lueGenePopDataPop)
export(lueNimi)
export(noIndex)
export(ownNum2Str)
export(poistaLiianPienet)
export(proportion2str)
export(randdir)
export(rivinSisaltamienMjonojenLkm)
export(selvitaDigitFormat)
export(simulateAllFreqs)
export(simulateIndividuals)
export(simuloiAlleeli)
export(suoritaMuutos)
export(takeLine)
export(testaaKoordinaatit)
export(testaaOnkoKunnollinenBapsData)
export(testaaPop)
export(writeMixtureInfo)
export(writeMixtureInfoPop)
importFrom(R6,R6Class) importFrom(R6,R6Class)
importFrom(Rsamtools,scanBam) importFrom(Rsamtools,scanBam)
importFrom(adegenet,.readExt) importFrom(adegenet,.readExt)

6
NEWS.md Normal file
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@ -0,0 +1,6 @@
# rBAPS (development version)
# rBAPS 0.0.0.9021
* Added a `NEWS.md` file to track changes to the package.
* Exported `greedyMix()` and `load_fasta()` functions.

1
R/addAlleles.R
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@ -4,7 +4,6 @@
#' @param line line #' @param line line
#' @param divider divider #' @param divider divider
#' @return data (after alleles were added) #' @return data (after alleles were added)
#' @export
addAlleles <- function(data, ind, line, divider) { addAlleles <- function(data, ind, line, divider) {
# Lisaa BAPS-formaatissa olevaan datataulukkoon # Lisaa BAPS-formaatissa olevaan datataulukkoon
# yksil<69><6C> ind vastaavat rivit. Yksil<69>n alleelit # yksil<69><6C> ind vastaavat rivit. Yksil<69>n alleelit

1
R/admix1.R
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@ -6,7 +6,6 @@
#' alleleCodes, adjprior, popnames, rowsFromInd, data, npops, noalle #' alleleCodes, adjprior, popnames, rowsFromInd, data, npops, noalle
#' @param tietue tietue #' @param tietue tietue
#' @importFrom methods is #' @importFrom methods is
#' @export
admix1 <- function(tietue) { admix1 <- function(tietue) {
if (!is.list(tietue)) { if (!is.list(tietue)) {
message("Load mixture result file. These are the files in this directory:") message("Load mixture result file. These are the files in this directory:")

1
R/calculatePopLogml.R
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@ -4,7 +4,6 @@
#' for the mean parameter. #' for the mean parameter.
#' @param points points #' @param points points
#' @param fii fii #' @param fii fii
#' @export
calculatePopLogml <- function(points, fii) { calculatePopLogml <- function(points, fii) {
n <- length(points) n <- length(points)
fuzzy_ones <- sum(points) fuzzy_ones <- sum(points)

23
R/comparePartitions.R Normal file
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@ -0,0 +1,23 @@
comparePartitions <- function(data, c.rows, partitionCompare.partitions, ninds, rowsFromInd, noalle, adjprior) {
stop("Comparing partitions not yet implemented") # TODO: implement
# nsamplingunits = size(c.rows,1);
# partitions = partitionCompare.partitions;
# npartitions = size(partitions,2);
# partitionLogml = zeros(1,npartitions);
# for i = 1:npartitions
# % number of unique partition lables
# npops = length(unique(partitions(:,i)));
# partitionInd = zeros(ninds*rowsFromInd,1);
# partitionSample = partitions(:,i);
# for j = 1:nsamplingunits
# partitionInd([c.rows(j,1):c.rows(j,2)]) = partitionSample(j);
# end
# partitionLogml(i) = initialCounts(partitionInd, data(:,1:end-1), npops, c.rows, noalle, adjprior);
# end
# % return the logml result
# partitionCompare.logmls = partitionLogml;
# set(h1, 'userdata', partitionCompare);
# return
}

1
R/computeAllFreqs2.R
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@ -2,7 +2,6 @@
#' @description Lisää a priori jokaista alleelia joka populaation joka lokukseen #' @description Lisää a priori jokaista alleelia joka populaation joka lokukseen
#' j 1/noalle(j) verran. #' j 1/noalle(j) verran.
#' @param noalle noalle #' @param noalle noalle
#' @export
computeAllFreqs2 <- function(noalle) { computeAllFreqs2 <- function(noalle) {
COUNTS <- ifelse(isGlobalEmpty(COUNTS), vector(), COUNTS) COUNTS <- ifelse(isGlobalEmpty(COUNTS), vector(), COUNTS)
SUMCOUNTS <- ifelse(isGlobalEmpty(SUMCOUNTS), vector(), COUNTS) SUMCOUNTS <- ifelse(isGlobalEmpty(SUMCOUNTS), vector(), COUNTS)

1
R/computeIndLogml.R
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@ -3,7 +3,6 @@
#' määritellyiksi kuten osuusTaulu:ssa. #' määritellyiksi kuten osuusTaulu:ssa.
#' @param omaFreqs own Freqs? #' @param omaFreqs own Freqs?
#' @param osuusTaulu Percentage table? #' @param osuusTaulu Percentage table?
#' @export
computeIndLogml <- function(omaFreqs, osuusTaulu) { computeIndLogml <- function(omaFreqs, osuusTaulu) {
omaFreqs <- as.matrix(omaFreqs) omaFreqs <- as.matrix(omaFreqs)
osuusTaulu <- as.matrix(osuusTaulu) osuusTaulu <- as.matrix(osuusTaulu)

2
R/computePersonalAllFreqs.R
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@ -7,8 +7,6 @@
#' @param data data #' @param data data
#' @param allFreqs allFreqs #' @param allFreqs allFreqs
#' @param rowsFromInd rowsFromInd #' @param rowsFromInd rowsFromInd
#' @export
computePersonalAllFreqs <- function(ind, data, allFreqs, rowsFromInd) { computePersonalAllFreqs <- function(ind, data, allFreqs, rowsFromInd) {
if (isGlobalEmpty(COUNTS)) { if (isGlobalEmpty(COUNTS)) {
nloci <- npops <- 1 nloci <- npops <- 1

39
R/computePopulationLogml.R
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@ -4,9 +4,13 @@ computePopulationLogml <- function(pops, adjprior, priorTerm) {
# ======================================================== # # ======================================================== #
# Limiting COUNTS size # # Limiting COUNTS size #
# ======================================================== # # ======================================================== #
COUNTS <- COUNTS[ if (!is.null(adjprior)) {
seq_len(nrow(adjprior)), seq_len(ncol(adjprior)), pops, drop = FALSE nr <- seq_len(nrow(adjprior))
] nc <- seq_len(ncol(adjprior))
COUNTS <- COUNTS[nr, nc, pops, drop = FALSE]
} else {
COUNTS <- NA
}
x <- size(COUNTS, 1) x <- size(COUNTS, 1)
y <- size(COUNTS, 2) y <- size(COUNTS, 2)
@ -15,25 +19,24 @@ computePopulationLogml <- function(pops, adjprior, priorTerm) {
# ======================================================== # # ======================================================== #
# Computation # # Computation #
# ======================================================== # # ======================================================== #
isarray <- length(dim(repmat(adjprior, c(1, 1, length(pops))))) > 2 term1 <- NULL
term1 <- squeeze( if (!is.null(adjprior)) {
sum( isarray <- length(dim(repmat(adjprior, c(1, 1, length(pops))))) > 2
term1 <- squeeze(
sum( sum(
reshape( sum(
lgamma( reshape(
repmat(adjprior, c(1, 1, length(pops))) + lgamma(
COUNTS[ repmat(adjprior, c(1, 1, length(pops))) + COUNTS[nr, nc, pops, drop = !isarray]
seq_len(nrow(adjprior)), seq_len(ncol(adjprior)), pops, ),
drop = !isarray c(x, y, z)
]
), ),
c(x, y, z) 1
), ),
1 2
), )
2
) )
) }
if (is.null(priorTerm)) priorTerm <- 0 if (is.null(priorTerm)) priorTerm <- 0
popLogml <- term1 - sum(lgamma(1 + SUMCOUNTS[pops, ]), 2) - priorTerm popLogml <- term1 - sum(lgamma(1 + SUMCOUNTS[pops, ]), 2) - priorTerm
return(popLogml) return(popLogml)

1
R/computeRows.R
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@ -4,7 +4,6 @@
#' @param rowsFromInd rowsFromInd #' @param rowsFromInd rowsFromInd
#' @param inds matrix #' @param inds matrix
#' @param ninds ninds #' @param ninds ninds
#' @export
computeRows <- function(rowsFromInd, inds, ninds) { computeRows <- function(rowsFromInd, inds, ninds) {
if (!is(inds, "matrix")) inds <- as.matrix(inds) if (!is(inds, "matrix")) inds <- as.matrix(inds)
if (identical(dim(inds), c(nrow(inds), 1L))) { if (identical(dim(inds), c(nrow(inds), 1L))) {

1
R/etsiParas.R
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@ -1,4 +1,3 @@
#' @export
#' @title Etsi Paras #' @title Etsi Paras
#' @description Search for the best? #' @description Search for the best?
#' @param osuus Percentages? #' @param osuus Percentages?

2
R/fgetl-fopen.R
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@ -6,7 +6,6 @@
#' fgetl returns tline as a numeric value -1. #' fgetl returns tline as a numeric value -1.
#' @author Waldir Leoncio #' @author Waldir Leoncio
#' @seealso fopen #' @seealso fopen
#' @export
fgetl <- function(file) { fgetl <- function(file) {
# ========================================================================== # ==========================================================================
# Validation # Validation
@ -27,5 +26,4 @@ fgetl <- function(file) {
#' @return The same as `readLines(filename)` #' @return The same as `readLines(filename)`
#' @author Waldir Leoncio #' @author Waldir Leoncio
#' @seealso fgetl #' @seealso fgetl
#' @export
fopen <- function(filename) readLines(filename) fopen <- function(filename) readLines(filename)

96
R/greedyMix.R
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@ -1,6 +1,16 @@
#' @title Clustering of individuals #' @title Clustering of individuals
#' @param data data file #' @param data data file
#' @param format Data format. Format supported: "FASTA", "VCF" ,"BAM", "GenePop" #' @param format Data format. Format supported: "FASTA", "VCF" ,"BAM", "GenePop"
#' @param partitionCompare a list of partitions to compare
#' @param ninds number of individuals
#' @param npops number of populations
#' @param counts counts
#' @param sumcounts sumcounts
#' @param max_iter maximum number of iterations
#' @param alleleCodes allele codes
#' @param inp input file
#' @param popnames population names
#' @param fixedK if \code{TRUE}, the number of populations is fixed
#' @param verbose if \code{TRUE}, prints extra output information #' @param verbose if \code{TRUE}, prints extra output information
#' @importFrom utils read.delim #' @importFrom utils read.delim
#' @importFrom vcfR read.vcfR #' @importFrom vcfR read.vcfR
@ -9,41 +19,59 @@
#' @references Samtools: a suite of programs for interacting #' @references Samtools: a suite of programs for interacting
#' with high-throughput sequencing data. <http://www.htslib.org/> #' with high-throughput sequencing data. <http://www.htslib.org/>
#' @export #' @export
greedyMix <- function(data, format, verbose = TRUE) { #' @examples
# Parsing data format ------------------------------------------------------ #' data <- system.file("extdata", "FASTA_clustering_haploid.fasta", package = "rBAPS")
#' greedyMix(data, "fasta")
greedyMix <- function(
data, format, partitionCompare = NULL, ninds = 1L, npops = 1L,
counts = NULL, sumcounts = NULL, max_iter = 100L, alleleCodes = NULL,
inp = NULL, popnames = NULL, fixedK = FALSE, verbose = FALSE
) {
# Importing and handling data ================================================
data <- importFile(data, format, verbose)
data <- handleData(data, tolower(format))
c <- list(
noalle = data[["noalle"]],
data = data[["newData"]],
adjprior = data[["adjprior"]],
priorTerm = data[["priorTerm"]],
rowsFromInd = data[["rowsFromInd"]]
)
if (missing(format)) { # Comparing partitions =======================================================
format <- gsub(".*\\.(.+)$", "\\1", data) if (!is.null(partitionCompare)) {
message("Format not provided. Guessing from file extension: ", format) logmls <- comparePartitions(
} c[["data"]], nrow(c[["data"]]), partitionCompare[["partitions"]], ninds,
format <- tolower(format) c[["rowsFromInd"]], c[["noalle"]], c[["adjprior"]]
# Dispatching to proper loading function -----------------------------------
if (format == "fasta") {
out <- load_fasta(data)
} else if (format == "vcf") {
out <- vcfR::read.vcfR(data, verbose = verbose)
} else if (format == "sam") {
stop(
"SAM files not directly supported. ",
"Install the samtools software and execute\n\n",
"samtools view -b ", data, " > out_file.bam\n\nto convert to BAM ",
"and try running this function again with 'format=BAM'"
) )
} else if (format == "bam") {
out <- Rsamtools::scanBam(data)
} else if (format == "genepop") {
if (toupper(adegenet::.readExt(data)) == "TXT") {
message("Creating a copy of the file with the .gen extension")
dataGen <- gsub("txt", "gen", data)
file.copy(data, dataGen)
out <- adegenet::read.genepop(dataGen)
} else {
out <- adegenet::read.genepop(data)
}
} else {
stop("Format not supported.")
} }
return(out)
# Generating partition summary ===============================================
ekat <- seq(1L, c[["rowsFromInd"]], ninds * c[["rowsFromInd"]]) # ekat = (1:rowsFromInd:ninds*rowsFromInd)';
c[["rows"]] <- c(ekat, ekat + c[["rowsFromInd"]] - 1L) # c.rows = [ekat ekat+rowsFromInd-1]
logml_npops_partitionSummary <- indMixWrapper(c, npops, counts, sumcounts, max_iter, fixedK, verbose)
logml <- logml_npops_partitionSummary[["logml"]]
npops <- logml_npops_partitionSummary[["npops"]]
partitionSummary <- logml_npops_partitionSummary[["partitionSummary"]]
# Generating output object ===================================================
out <- list(
"alleleCodes" = alleleCodes, "adjprior" = c[["adjprior"]],
"popnames" = popnames, "rowsFromInd" = c[["rowsFromInd"]],
"data" = c[["data"]], "npops" = npops, "noalle" = c[["noalle"]],
"mixtureType" = "mix", "logml" = logml
)
if (logml == 1) {
return(out)
}
# Writing mixture info =======================================================
changesInLogml <- writeMixtureInfo(
logml, c[["rowsFromInd"]], c[["data"]], c[["adjprior"]], c[["priorTerm"]],
NULL, inp, partitionSummary, popnames, fixedK
)
# Updateing results ==========================================================
return(c(out, "changesInLogml" = changesInLogml))
} }

1
R/greedyPopMix.R
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@ -11,7 +11,6 @@
#' @importFrom matlab2r uiputfile #' @importFrom matlab2r uiputfile
#' @references Samtools: a suite of programs for interacting #' @references Samtools: a suite of programs for interacting
#' with high-throughput sequencing data. <http://www.htslib.org/> #' with high-throughput sequencing data. <http://www.htslib.org/>
#' @export
greedyPopMix <- function(data, format, partitionCompare = NULL, verbose = TRUE greedyPopMix <- function(data, format, partitionCompare = NULL, verbose = TRUE
) { ) {
# Replacing original file reading code with greedyMix() # Replacing original file reading code with greedyMix()

36
R/handleData.R
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@ -1,5 +1,6 @@
#' @title Handle Data #' @title Handle Data
#' @param raw_data Raw data #' @param raw_data Raw data in Genepop or BAPS format
#' @param format data format
#' @details The last column of the original data tells you from which #' @details The last column of the original data tells you from which
#' individual that line is from. The function first examines how many line #' individual that line is from. The function first examines how many line
#' maximum is from one individual giving know if it is haploid, diploid, etc. #' maximum is from one individual giving know if it is haploid, diploid, etc.
@ -7,9 +8,9 @@
#' maximum. If the code of an allele is = 0, the function changes that allele #' maximum. If the code of an allele is = 0, the function changes that allele
#' code to the smallest code that is larger than any code in use. After this, #' code to the smallest code that is larger than any code in use. After this,
#' the function changes the allele codes so that one locus j #' the function changes the allele codes so that one locus j
#' codes get values between? 1, ..., Noah (j). #' codes get values between? 1, ..., noalle(j).
#' @export #' @export
handleData <- function(raw_data) { handleData <- function(raw_data, format = "Genepop") {
# Alkuper?isen datan viimeinen sarake kertoo, milt?yksil?lt? # Alkuper?isen datan viimeinen sarake kertoo, milt?yksil?lt?
# kyseinen rivi on per?isin. Funktio tutkii ensin, ett?montako # kyseinen rivi on per?isin. Funktio tutkii ensin, ett?montako
# rivi?maksimissaan on per?isin yhdelt?yksil?lt? jolloin saadaan # rivi?maksimissaan on per?isin yhdelt?yksil?lt? jolloin saadaan
@ -20,28 +21,29 @@ handleData <- function(raw_data) {
# koodi pienimm?ksi koodiksi, joka isompi kuin mik??n k?yt?ss?oleva koodi. # koodi pienimm?ksi koodiksi, joka isompi kuin mik??n k?yt?ss?oleva koodi.
# T?m?n j?lkeen funktio muuttaa alleelikoodit siten, ett?yhden lokuksen j # T?m?n j?lkeen funktio muuttaa alleelikoodit siten, ett?yhden lokuksen j
# koodit saavat arvoja v?lill?1,...,noalle(j). # koodit saavat arvoja v?lill?1,...,noalle(j).
nloci <- switch(
tolower(format),
"genepop" = ncol(raw_data) - 1L,
"baps" = ncol(raw_data) - 1L,
"fasta" = ncol(raw_data),
"vcf" = stop("VCF format not supported for processing yet"),
"bam" = stop("BAM format not supported for processing yet")
)
data <- as.matrix(raw_data) data <- as.matrix(raw_data)
nloci <- size(raw_data, 2) - 1
dataApu <- data[, 1:nloci] dataApu <- data[, 1:nloci]
nollat <- matlab2r::find(dataApu == 0) nollat <- matlab2r::find(dataApu == 0)
if (!isempty(nollat)) { if (!isempty(nollat)) {
isoinAlleeli <- base::max(max(dataApu)) isoinAlleeli <- base::max(base::max(dataApu))
dataApu[nollat] <- isoinAlleeli + 1 dataApu[nollat] <- isoinAlleeli + 1
data[, 1:nloci] <- dataApu data[, 1:nloci] <- dataApu
} }
# dataApu <- []
# nollat <- []
# isoinAlleeli <- []
noalle <- zeros(1, nloci) noalle <- zeros(1, nloci)
alleelitLokuksessa <- cell(nloci, 1, expandable = TRUE) alleelitLokuksessa <- cell(nloci, 1, expandable = TRUE)
for (i in 1:nloci) { for (i in 1:nloci) {
alleelitLokuksessaI <- unique(data[, i]) alleelitLokuksessaI <- unique(data[, i])
alleelitLokuksessa[[i]] <- sort(alleelitLokuksessaI[ alleelitLokuksessa[[i]] <- sort(alleelitLokuksessaI[
matlab2r::find( matlab2r::find(alleelitLokuksessaI >= 0)
alleelitLokuksessaI >= 0
)
]) ])
noalle[i] <- length(alleelitLokuksessa[[i]]) noalle[i] <- length(alleelitLokuksessa[[i]])
} }
@ -49,9 +51,7 @@ handleData <- function(raw_data) {
for (i in 1:nloci) { for (i in 1:nloci) {
alleelitLokuksessaI <- alleelitLokuksessa[[i]] alleelitLokuksessaI <- alleelitLokuksessa[[i]]
puuttuvia <- base::max(noalle) - length(alleelitLokuksessaI) puuttuvia <- base::max(noalle) - length(alleelitLokuksessaI)
alleleCodes[, i] <- as.matrix( alleleCodes[, i] <- as.matrix(c(alleelitLokuksessaI, zeros(puuttuvia, 1)))
c(alleelitLokuksessaI, zeros(puuttuvia, 1))
)
} }
for (loc in seq_len(nloci)) { for (loc in seq_len(nloci)) {
@ -60,7 +60,7 @@ handleData <- function(raw_data) {
} }
} }
nind <- base::max(data[, ncol(data)]) nind <- as.integer(base::max(data[, ncol(data)]))
nrows <- size(data, 1) nrows <- size(data, 1)
ncols <- size(data, 2) ncols <- size(data, 2)
rowsFromInd <- zeros(nind, 1) rowsFromInd <- zeros(nind, 1)
@ -71,11 +71,11 @@ handleData <- function(raw_data) {
a <- -999 a <- -999
emptyRow <- repmat(a, c(1, ncols)) emptyRow <- repmat(a, c(1, ncols))
lessThanMax <- matlab2r::find(rowsFromInd < maxRowsFromInd) lessThanMax <- matlab2r::find(rowsFromInd < maxRowsFromInd)
missingRows <- maxRowsFromInd * nind - nrows missingRows <- max(maxRowsFromInd * nind - nrows, 0L)
data <- rbind(data, zeros(missingRows, ncols)) data <- rbind(data, zeros(missingRows, ncols))
pointer <- 1 pointer <- 1
for (ind in t(lessThanMax)) { # K?y l?pi ne yksil?t, joilta puuttuu rivej? for (ind in t(lessThanMax)) { # K?y l?pi ne yksil?t, joilta puuttuu rivej?
miss <- maxRowsFromInd - rowsFromInd(ind) # T?lt?yksil?lt?puuttuvien lkm. miss <- maxRowsFromInd - rowsFromInd[ind] # T?lt?yksil?lt?puuttuvien lkm.
} }
data <- sortrows(data, ncols) # Sorttaa yksil?iden mukaisesti data <- sortrows(data, ncols) # Sorttaa yksil?iden mukaisesti
newData <- data newData <- data

1
R/handlePopData.R
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@ -4,7 +4,6 @@
#' codes so that the codes for one locus have values between 1 and noalle[j]. #' codes so that the codes for one locus have values between 1 and noalle[j].
#' Before this change, an allele whose code is zero is changed. #' Before this change, an allele whose code is zero is changed.
#' @param raw_data raw data #' @param raw_data raw data
#' @export
handlePopData <- function(raw_data) { handlePopData <- function(raw_data) {
# Alkuperäisen datan viimeinen sarake kertoo, milt?yksilölt? # Alkuperäisen datan viimeinen sarake kertoo, milt?yksilölt?
# kyseinen rivi on peräisin. Funktio muuttaa alleelikoodit # kyseinen rivi on peräisin. Funktio muuttaa alleelikoodit

49
R/importFile.R Normal file
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@ -0,0 +1,49 @@
#' @title Import data file
#' @description Imports data from several formats (FASTA, VCF, SAM, BAM,
#' Genepop).
#' @param data raw dataset
#' @param format data format (guesses from extension if not provided)
#' @param verbose if \code{TRUE}, prints extra output information
#' @return The data in a format that can be used by the other functions
#' @export
#' @examples
#' path_inst <- system.file("extdata", "", package = "rBAPS")
#' importFile(file.path(path_inst, "FASTA_clustering_haploid.fasta"))
importFile <- function(data, format, verbose) {
# Parsing data format ------------------------------------------------------
if (missing(format)) {
format <- gsub(".*\\.(.+)$", "\\1", data)
message("Format not provided. Guessing from file extension: ", format)
}
format <- tolower(format)
# Dispatching to proper loading function -----------------------------------
if (format == "fasta") {
out <- load_fasta(data)
} else if (format == "vcf") {
out <- vcfR::read.vcfR(data, verbose = verbose)
} else if (format == "sam") {
stop(
"SAM files not directly supported. ",
"Install the samtools software and execute\n\n",
"samtools view -b ", data, " > out_file.bam\n\nto convert to BAM ",
"and try running this function again with 'format=BAM'"
)
} else if (format == "bam") {
out <- Rsamtools::scanBam(data)
} else if (format == "genepop") {
if (toupper(adegenet::.readExt(data)) == "TXT") {
message("Creating a copy of the file with the .gen extension")
dataGen <- gsub("txt", "gen", data)
file.copy(data, dataGen)
out <- adegenet::read.genepop(dataGen)
} else {
out <- adegenet::read.genepop(data)
}
} else {
stop("Format not supported.")
}
return(out)
}

50
R/indMix.R
View file

@ -1,4 +1,4 @@
indMix <- function(c, npops, dispText = TRUE) { indMix <- function(c, npops, counts = NULL, sumcounts = NULL, max_iter = 100L, dispText = FALSE) {
# Greedy search algorithm with unknown number of classes for regular # Greedy search algorithm with unknown number of classes for regular
# clustering. # clustering.
# Input npops is not used if called by greedyMix or greedyPopMix. # Input npops is not used if called by greedyMix or greedyPopMix.
@ -17,8 +17,11 @@ indMix <- function(c, npops, dispText = TRUE) {
if (isfield(c, "dist")) { if (isfield(c, "dist")) {
dist <- c$dist dist <- c$dist
Z <- c$Z Z <- c$Z
} else {
Z <- NULL
} }
rm(c) rm(c)
nargin <- length(as.list(match.call())) - 1 nargin <- length(as.list(match.call())) - 1
if (nargin < 2) { if (nargin < 2) {
@ -65,14 +68,14 @@ indMix <- function(c, npops, dispText = TRUE) {
nruns <- length(npopsTaulu) nruns <- length(npopsTaulu)
initData <- data initData <- data
data <- data[, 1:(ncol(data) - 1)] data <- data[, seq_along(noalle)] # Original code always dropped last column.
logmlBest <- -1e50 logmlBest <- -1e50
partitionSummary <- -1e50 * ones(30, 2) # Tiedot 30 parhaasta partitiosta (npops ja logml) partitionSummary <- -1e50 * ones(30, 2) # Tiedot 30 parhaasta partitiosta (npops ja logml)
partitionSummary[, 1] <- zeros(30, 1) partitionSummary[, 1] <- zeros(30, 1)
worstLogml <- -1e50 worstLogml <- -1e50
worstIndex <- 1 worstIndex <- 1
for (run in 1:nruns) { for (run in seq_along(nruns)) {
npops <- npopsTaulu[[run]] npops <- npopsTaulu[[run]]
if (dispText) { if (dispText) {
dispLine() dispLine()
@ -84,6 +87,7 @@ indMix <- function(c, npops, dispText = TRUE) {
) )
} }
ninds <- size(rows, 1) ninds <- size(rows, 1)
initialPartition <- admixture_initialization(initData, npops, Z) initialPartition <- admixture_initialization(initData, npops, Z)
sumcounts_counts_logml <- initialCounts( sumcounts_counts_logml <- initialCounts(
initialPartition, data, npops, rows, noalle, adjprior initialPartition, data, npops, rows, noalle, adjprior
@ -93,16 +97,15 @@ indMix <- function(c, npops, dispText = TRUE) {
logml <- sumcounts_counts_logml$logml logml <- sumcounts_counts_logml$logml
PARTITION <- zeros(ninds, 1) PARTITION <- zeros(ninds, 1)
for (i in 1:ninds) { for (i in seq_len(ninds)) {
apu <- rows[i] apu <- rows[i]
PARTITION[i] <- initialPartition[apu[1]] PARTITION[i] <- initialPartition[apu[1]]
} }
COUNTS <- counts COUNTS <- counts
SUMCOUNTS <- sumcounts SUMCOUNTS <- sumcounts
POP_LOGML <- computePopulationLogml(1:npops, adjprior, priorTerm) POP_LOGML <- computePopulationLogml(seq_len(npops), adjprior, priorTerm)
LOGDIFF <- repmat(-Inf, c(ninds, npops)) LOGDIFF <- repmat(-Inf, c(ninds, npops))
rm(initialPartition, counts, sumcounts)
# PARHAAN MIXTURE-PARTITION ETSIMINEN # PARHAAN MIXTURE-PARTITION ETSIMINEN
nRoundTypes <- 7 nRoundTypes <- 7
@ -120,30 +123,34 @@ indMix <- function(c, npops, dispText = TRUE) {
) )
} }
iter <- 1L
while (ready != 1) { while (ready != 1) {
# FIXME: loop caught in here iter <- iter + 1L
if (iter > max_iter) {
warning("max_iter reached. Stopping prematurely.")
break
}
muutoksia <- 0 muutoksia <- 0
if (dispText) { if (dispText) {
message(paste("\nPerforming steps:", as.character(roundTypes))) message("Performing steps: ", paste(roundTypes, collapse = " "))
} }
for (n in 1:length(roundTypes)) { for (n in seq_along(roundTypes)) {
round <- roundTypes[n] round <- roundTypes[n]
kivaluku <- 0 kivaluku <- 0
if (kokeiltu[round] == 1) { # Askelta kokeiltu viime muutoksen j<>lkeen if (kokeiltu[round] == 1) { # Askelta kokeiltu viime muutoksen j<>lkeen
} else if (round == 0 | round == 1) { # Yksil<69>n siirt<72>minen toiseen populaatioon. } else if (round == 0 | round == 1) { # Yksil<69>n siirt<72>minen toiseen populaatioon.
inds <- 1:ninds inds <- seq_len(ninds)
aputaulu <- cbind(inds, rand(ninds, 1)) aputaulu <- cbind(t(inds), rand(ninds, 1))
aputaulu <- sortrows(aputaulu, 2) aputaulu <- matrix(sortrows(aputaulu, 2), nrow = nrow(aputaulu))
inds <- t(aputaulu[, 1]) inds <- t(aputaulu[, 1])
muutosNyt <- 0 muutosNyt <- 0
for (ind in inds) { for (ind in inds) {
i1 <- PARTITION[ind] i1 <- PARTITION[ind]
muutokset_diffInCounts <- greedyMix_muutokset$new() muutokset_diffInCounts <- greedyMix_muutokset$new()
# FIXME: using 100-length global variables instead of the ones in this function
muutokset_diffInCounts <- muutokset_diffInCounts$laskeMuutokset( muutokset_diffInCounts <- muutokset_diffInCounts$laskeMuutokset(
ind, rows, data, adjprior, priorTerm ind, rows, data, adjprior, priorTerm
) )
@ -190,7 +197,7 @@ indMix <- function(c, npops, dispText = TRUE) {
} }
} else if (round == 2) { # Populaation yhdist<73>minen toiseen. } else if (round == 2) { # Populaation yhdist<73>minen toiseen.
maxMuutos <- 0 maxMuutos <- 0
for (pop in 1:npops) { for (pop in seq_len(npops)) {
muutokset_diffInCounts <- greedyMix_muutokset$new() muutokset_diffInCounts <- greedyMix_muutokset$new()
muutokset_diffInCounts <- muutokset_diffInCounts$laskeMuutokset2( muutokset_diffInCounts <- muutokset_diffInCounts$laskeMuutokset2(
pop, rows, data, adjprior, priorTerm pop, rows, data, adjprior, priorTerm
@ -234,7 +241,7 @@ indMix <- function(c, npops, dispText = TRUE) {
} else if (round == 3 || round == 4) { # Populaation jakaminen osiin. } else if (round == 3 || round == 4) { # Populaation jakaminen osiin.
maxMuutos <- 0 maxMuutos <- 0
ninds <- size(rows, 1) ninds <- size(rows, 1)
for (pop in 1:npops) { for (pop in seq_len(npops)) {
inds2 <- matlab2r::find(PARTITION == pop) inds2 <- matlab2r::find(PARTITION == pop)
ninds2 <- length(inds2) ninds2 <- length(inds2)
if (ninds2 > 2) { if (ninds2 > 2) {
@ -265,7 +272,7 @@ indMix <- function(c, npops, dispText = TRUE) {
muutoksia <- 1 muutoksia <- 1
kokeiltu <- zeros(nRoundTypes, 1) kokeiltu <- zeros(nRoundTypes, 1)
rivit <- list() rivit <- list()
for (i in 1:length(muuttuvat)) { for (i in seq_len(muuttuvat)) {
ind <- muuttuvat[i] ind <- muuttuvat[i]
lisa <- rows[ind, 1]:rows[ind, 2] lisa <- rows[ind, 1]:rows[ind, 2]
rivit <- rbind(rivit, t(lisa)) rivit <- rbind(rivit, t(lisa))
@ -421,7 +428,7 @@ indMix <- function(c, npops, dispText = TRUE) {
totalMuutos <- muutokset(1, emptyPop) totalMuutos <- muutokset(1, emptyPop)
rivit <- list() rivit <- list()
for (i in 1:length(muuttuvat)) { for (i in seq_len(muuttuvat)) {
ind <- muuttuvat[i] ind <- muuttuvat[i]
lisa <- rows[ind, 1]:rows[ind, 2] lisa <- rows[ind, 1]:rows[ind, 2]
rivit <- c(rivit, lisa) rivit <- c(rivit, lisa)
@ -506,8 +513,6 @@ indMix <- function(c, npops, dispText = TRUE) {
} }
} }
} }
# FIXME: muutoksia is never 0, so vaihe never equals 5 and ready 1
print(paste("i1 =", i1, "i2 =", i2, "maxMuutos =", maxMuutos)) # TEMP
if (muutoksia == 0) { if (muutoksia == 0) {
if (vaihe <= 4) { if (vaihe <= 4) {
vaihe <= vaihe + 1 vaihe <= vaihe + 1
@ -536,11 +541,10 @@ indMix <- function(c, npops, dispText = TRUE) {
# TALLENNETAAN # TALLENNETAAN
npops <- poistaTyhjatPopulaatiot(npops) npops <- poistaTyhjatPopulaatiot(npops)
POP_LOGML <- computePopulationLogml(1:npops, adjprior, priorTerm) POP_LOGML <- computePopulationLogml(seq_len(npops), adjprior, priorTerm)
if (dispText) { if (dispText) {
print(paste("Found partition with", as.character(npops), "populations.")) message("Found partition with ", as.character(npops), " populations.")
print(paste("Log(ml) =", as.character(logml))) message("Log(ml) = ", as.character(logml))
print(" ")
} }
if (logml > logmlBest) { if (logml > logmlBest) {

7
R/indMixWrapper.R Normal file
View file

@ -0,0 +1,7 @@
indMixWrapper <- function(c, npops, counts, sumcounts, max_iter, fixedK = FALSE, verbose = FALSE) {
if (fixedK) {
stop("indMix_fixK() not yet implemented.") # TODO: translate indMix_fixK.m
} else {
indMix(c, npops, counts, sumcounts, max_iter, verbose)
}
}

1
R/initPopNames.R
View file

@ -1,7 +1,6 @@
#' @title Initialize Pop Names #' @title Initialize Pop Names
#' @param nameFile nameFile #' @param nameFile nameFile
#' @param indexFile indexFile #' @param indexFile indexFile
#' @export
initPopNames <- function(nameFile, indexFile) { initPopNames <- function(nameFile, indexFile) {
# Palauttaa tyhj<68>n, mik<69>li nimitiedosto ja indeksitiedosto # Palauttaa tyhj<68>n, mik<69>li nimitiedosto ja indeksitiedosto
# eiv<69>t olleet yht?pitki? # eiv<69>t olleet yht?pitki?

2
R/initialCounts.R
View file

@ -2,7 +2,7 @@ initialCounts <- function(partition, data, npops, rows, noalle, adjprior) {
nloci <- size(data, 2) nloci <- size(data, 2)
ninds <- size(rows, 1) ninds <- size(rows, 1)
koot <- rows[, 1] - rows[, 2] + 1 koot <- rows[1] - rows[2] + 1
maxSize <- base::max(koot) maxSize <- base::max(koot)
counts <- zeros(base::max(noalle), nloci, npops) counts <- zeros(base::max(noalle), nloci, npops)

4
R/laskeLoggis.R
View file

@ -1,7 +1,7 @@
laskeLoggis <- function(counts, sumcounts, adjprior) { laskeLoggis <- function(counts, sumcounts, adjprior) {
npops <- size(counts, 3) npops <- size(counts, 3)
replicated_adjprior <- array(adjprior, c(nrow(adjprior), ncol(adjprior), npops))
sum1 <- sum(sum(sum(lgamma(counts + repmat(adjprior, c(1, 1, npops)))))) sum1 <- sum(sum(sum(lgamma(counts + replicated_adjprior))))
sum3 <- sum(sum(lgamma(adjprior))) - sum(sum(lgamma(1 + sumcounts))) sum3 <- sum(sum(lgamma(adjprior))) - sum(sum(lgamma(1 + sumcounts)))
logml2 <- sum1 - npops * sum3 logml2 <- sum1 - npops * sum3
loggis <- logml2 loggis <- logml2

7
R/laskeMuutokset12345.R
View file

@ -349,12 +349,15 @@ greedyMix_muutokset <- R6Class(
i1_logml <- POP_LOGML[i1] i1_logml <- POP_LOGML[i1]
muutokset[i1] <- 0 muutokset[i1] <- 0
rows <- globalRows[ind, 1]:globalRows[ind, 2] if (is.null(dim(globalRows))) {
rows <- globalRows[1]:globalRows[2]
} else {
rows <- globalRows[ind, 1]:globalRows[ind, 2]
}
diffInCounts <- computeDiffInCounts( diffInCounts <- computeDiffInCounts(
rows, size(COUNTS, 1), size(COUNTS, 2), data rows, size(COUNTS, 1), size(COUNTS, 2), data
) )
diffInSumCounts <- colSums(diffInCounts) diffInSumCounts <- colSums(diffInCounts)
COUNTS[, , i1] <- COUNTS[, , i1] - diffInCounts COUNTS[, , i1] <- COUNTS[, , i1] - diffInCounts
SUMCOUNTS[i1, ] <- SUMCOUNTS[i1, ] - diffInSumCounts SUMCOUNTS[i1, ] <- SUMCOUNTS[i1, ] - diffInSumCounts
new_i1_logml <- computePopulationLogml(i1, adjprior, priorTerm) new_i1_logml <- computePopulationLogml(i1, adjprior, priorTerm)

1
R/learn_partition_modified.R
View file

@ -1,5 +1,4 @@
#' @title Learn partition (modified) #' @title Learn partition (modified)
#' @export
#' @param ordered ordered #' @param ordered ordered
#' @return part #' @return part
#' @description This function is called only if some individual has less than #' @description This function is called only if some individual has less than

1
R/learn_simple_partition.R
View file

@ -3,7 +3,6 @@
#' @param fii fii #' @param fii fii
#' @description Goes through all the ways to divide the points into two or #' @description Goes through all the ways to divide the points into two or
#' three groups. Chooses the partition which obtains highest logml. #' three groups. Chooses the partition which obtains highest logml.
#' @export
learn_simple_partition <- function(ordered_points, fii) { learn_simple_partition <- function(ordered_points, fii) {
npoints <- length(ordered_points) npoints <- length(ordered_points)

1
R/linkage.R
View file

@ -13,7 +13,6 @@
#' that BAPS should use this function instead of the base one, so this is why #' that BAPS should use this function instead of the base one, so this is why
#' this function is part of this package (instead of a MATLAB-replicating #' this function is part of this package (instead of a MATLAB-replicating
#' package such as matlab2r) #' package such as matlab2r)
#' @export
linkage <- function(Y, method = "co") { linkage <- function(Y, method = "co") {
k <- size(Y)[1] k <- size(Y)[1]
n <- size(Y)[2] n <- size(Y)[2]

25
R/load_fasta.R
View file

@ -4,18 +4,19 @@
#' running the hierBAPS algorithm. #' running the hierBAPS algorithm.
#' #'
#' @param msa Either the location of a fasta file or ape DNAbin object containing the multiple sequence alignment data to be clustered #' @param msa Either the location of a fasta file or ape DNAbin object containing the multiple sequence alignment data to be clustered
#' @param keep.singletons A logical indicating whether to consider singleton mutations in calculating the clusters #' @param keep_singletons A logical indicating whether to consider singleton mutations in calculating the clusters
#' @param output_numbers A logical indicating whether to output the data as
#' numbers (TRUE) or letters (FALSE)
#' #'
#' @return A character matrix with filtered SNP data #' @return A character matrix with filtered SNP data
#' #'
#' @examples #' @examples
#' msa <- system.file("ext", "seqs.fa", package = "rBAPS") #' msa <- system.file("extdata", "seqs.fa", package = "rBAPS")
#' snp.matrix <- load_fasta(msa) #' snp.matrix <- rBAPS:::load_fasta(msa)
#' @author Gerry Tonkin-Hill, Waldir Leoncio #' @author Gerry Tonkin-Hill, Waldir Leoncio
#' @seealso rhierbaps::load_fasta #' @seealso rhierbaps::load_fasta
#' @importFrom ape read.FASTA as.DNAbin #' @importFrom ape read.FASTA as.DNAbin
#' @export load_fasta <- function(msa, keep_singletons = FALSE, output_numbers = TRUE) {
load_fasta <- function(msa, keep.singletons = FALSE) {
# Check inputs # Check inputs
if (is(msa, "character")) { if (is(msa, "character")) {
@ -28,7 +29,9 @@ load_fasta <- function(msa, keep.singletons = FALSE) {
} else { } else {
stop("incorrect input for msa!") stop("incorrect input for msa!")
} }
if (!is.logical(keep.singletons)) stop("Invalid keep.singletons! Must be on of TRUE/FALSE.") if (!is.logical(keep_singletons)) {
stop("Invalid keep_singletons! Must be one of TRUE/FALSE.")
}
# Load sequences using ape. This does a lot of the checking for us. # Load sequences using ape. This does a lot of the checking for us.
seq_names <- labels(seqs) seq_names <- labels(seqs)
@ -46,8 +49,8 @@ load_fasta <- function(msa, keep.singletons = FALSE) {
conserved <- colSums(t(t(seqs) == seqs[1, ])) == nrow(seqs) conserved <- colSums(t(t(seqs) == seqs[1, ])) == nrow(seqs)
seqs <- seqs[, !conserved] seqs <- seqs[, !conserved]
if (!keep.singletons) { if (!keep_singletons) {
# remove singletons as they are uninformative in the algorithm # remove_singletons as they are uninformative in the algorithm
is_singleton <- apply(seqs, 2, function(x) { is_singleton <- apply(seqs, 2, function(x) {
tab <- table(x) tab <- table(x)
return(x %in% names(tab)[tab == 1]) return(x %in% names(tab)[tab == 1])
@ -58,5 +61,11 @@ load_fasta <- function(msa, keep.singletons = FALSE) {
# Convert gaps and unknowns to same symbol # Convert gaps and unknowns to same symbol
seqs[seqs == "n"] <- "-" seqs[seqs == "n"] <- "-"
# Replace letters with numbers, dashes with zeros
if (output_numbers) {
seqs <- matrix(match(seqs, c("a", "c", "g", "t")), nrow(seqs))
seqs[is.na(seqs)] <- 0
}
return(seqs) return(seqs)
} }

1
R/logml2String.R
View file

@ -2,7 +2,6 @@
#' @description Returns a string representation of a logml #' @description Returns a string representation of a logml
#' @param logml input Logml #' @param logml input Logml
#' @return String version of logml #' @return String version of logml
#' @export
logml2String <- function(logml) { logml2String <- function(logml) {
# Palauttaa logml:n string-esityksen. # Palauttaa logml:n string-esityksen.
mjono <- " " mjono <- " "

1
R/lueGenePopData.R
View file

@ -2,7 +2,6 @@
#' @description Reads GenePop-formatted data #' @description Reads GenePop-formatted data
#' @param tiedostonNimi Name of the file #' @param tiedostonNimi Name of the file
#' @return list containing data and popnames #' @return list containing data and popnames
#' @export
lueGenePopData <- function(tiedostonNimi) { lueGenePopData <- function(tiedostonNimi) {
fid <- readLines(tiedostonNimi) fid <- readLines(tiedostonNimi)
line <- fid[1] # ensimmäinen rivi line <- fid[1] # ensimmäinen rivi

1
R/lueGenePopDataPop.R
View file

@ -3,7 +3,6 @@
#' group. popnames are as before. #' group. popnames are as before.
#' @param tiedostonNimi Name of the file #' @param tiedostonNimi Name of the file
#' @return List containing data and popnames #' @return List containing data and popnames
#' @export
lueGenePopDataPop <- function(tiedostonNimi) { lueGenePopDataPop <- function(tiedostonNimi) {
# Data annetaan muodossa, jossa viimeinen sarake kertoo ryhmän. # Data annetaan muodossa, jossa viimeinen sarake kertoo ryhmän.
# popnames on kuten ennenkin. # popnames on kuten ennenkin.

1
R/lueNimi.R
View file

@ -2,7 +2,6 @@
#' @description Returns the part of the line from the beginning that is before the comma. Useful for returning the name of a GenePop area #' @description Returns the part of the line from the beginning that is before the comma. Useful for returning the name of a GenePop area
#' @param line line #' @param line line
#' @return nimi #' @return nimi
#' @export
lueNimi <- function(line) { lueNimi <- function(line) {
# ========================================================================== # ==========================================================================
# Validation # Validation

1
R/noIndex.R
View file

@ -5,7 +5,6 @@
#' @return puredata: a data contains no index column. #' @return puredata: a data contains no index column.
#' @param data data #' @param data data
#' @param noalle noalle #' @param noalle noalle
#' @export
noIndex <- function(data, noalle) { noIndex <- function(data, noalle) {
limit <- ifelse(is(noalle, "matrix"), ncol(noalle), length(noalle)) limit <- ifelse(is(noalle, "matrix"), ncol(noalle), length(noalle))
if (size(data, 2) == limit + 1) { if (size(data, 2) == limit + 1) {

1
R/ownNum2Str.R
View file

@ -2,7 +2,6 @@
#' @description Converts numbers to strings #' @description Converts numbers to strings
#' @param number number #' @param number number
#' @note On Matlab, if number is NaN the output is 'NaN'. Here, the output will be an error. Also, the function belo expects "number" to have length one, whereas Matlab accepts vectors. #' @note On Matlab, if number is NaN the output is 'NaN'. Here, the output will be an error. Also, the function belo expects "number" to have length one, whereas Matlab accepts vectors.
#' @export
ownNum2Str <- function(number) { ownNum2Str <- function(number) {
absolute <- abs(number) absolute <- abs(number)
if (absolute < 1000) { if (absolute < 1000) {

1
R/poistaLiianPienet.R
View file

@ -5,7 +5,6 @@
#' @param npops npops #' @param npops npops
#' @param rowsFromInd rowsFromInd #' @param rowsFromInd rowsFromInd
#' @param alaraja alaraja #' @param alaraja alaraja
#' @export
poistaLiianPienet <- function(npops, rowsFromInd, alaraja) { poistaLiianPienet <- function(npops, rowsFromInd, alaraja) {
popSize <- zeros(1, npops) popSize <- zeros(1, npops)
if (npops > 0) { if (npops > 0) {

4
R/poistaTyhjatPopulaatiot.R
View file

@ -1,13 +1,13 @@
poistaTyhjatPopulaatiot <- function(npops) { poistaTyhjatPopulaatiot <- function(npops) {
# % Poistaa tyhjentyneet populaatiot COUNTS:ista ja # % Poistaa tyhjentyneet populaatiot COUNTS:ista ja
# % SUMCOUNTS:ista. P<>ivitt<74><74> npops:in ja PARTITION:in. # % SUMCOUNTS:ista. P<>ivitt<74><74> npops:in ja PARTITION:in.
notEmpty <- matlab2r::find(any(SUMCOUNTS, 2)) notEmpty <- matlab2r::find(apply(SUMCOUNTS, 1, function(x) any(x > 0)))
COUNTS <- COUNTS[, , notEmpty] COUNTS <- COUNTS[, , notEmpty]
SUMCOUNTS <- SUMCOUNTS[notEmpty, ] SUMCOUNTS <- SUMCOUNTS[notEmpty, ]
LOGDIFF <- LOGDIFF[, notEmpty] LOGDIFF <- LOGDIFF[, notEmpty]
for (n in 1:length(notEmpty)) { for (n in 1:length(notEmpty)) {
apu <- matlab2r::find(PARTITION == notEmpty(n)) apu <- matlab2r::find(PARTITION == notEmpty[n])
PARTITION[apu] <- n PARTITION[apu] <- n
} }
npops <- length(notEmpty) npops <- length(notEmpty)

1
R/proportion2str.R
View file

@ -3,7 +3,6 @@
#' @return a 4-mark presentation of proportion #' @return a 4-mark presentation of proportion
#' @note The `round` function in R, being ISO-compliant, rounds 8.5 to 8. The #' @note The `round` function in R, being ISO-compliant, rounds 8.5 to 8. The
#' Matlab equivalent rounds it to 9. #' Matlab equivalent rounds it to 9.
#' @export
proportion2str <- function(prob) { proportion2str <- function(prob) {
if (abs(prob) < 1e-3) { if (abs(prob) < 1e-3) {
str <- "0.00" str <- "0.00"

3
R/randdir.R
View file

@ -1,10 +1,9 @@
#' @title Generates random numbers #' @title Generates random numbers
#' @return vector of length `nc` with r.v. realizations from Gamma(rate=1) #' @return vector of length `nc` with r.v. realizations from Gamma(rate=1)
#' @examples randdir(matrix(c(10, 30, 60), 3), 3) #' @examples rBAPS:::randdir(matrix(c(10, 30, 60), 3), 3)
#' @param counts shape parameter #' @param counts shape parameter
#' @param nc number of rows on output #' @param nc number of rows on output
#' @seealso randga #' @seealso randga
#' @export
randdir <- function(counts, nc) { randdir <- function(counts, nc) {
svar <- zeros(nc, 1) svar <- zeros(nc, 1)
for (i in 1:nc) { for (i in 1:nc) {

1
R/rivinSisaltamienMjonojenLkm.R
View file

@ -2,7 +2,6 @@
#' @param line line number #' @param line line number
#' @return count #' @return count
#' @description Returns the number of queues contained in the line. There must be a space between the queues. #' @description Returns the number of queues contained in the line. There must be a space between the queues.
#' @export
rivinSisaltamienMjonojenLkm <- function(line) { rivinSisaltamienMjonojenLkm <- function(line) {
# Palauttaa line:n sis<69>lt<6C>mien mjonojen lukum<75><6D>r<EFBFBD>n. # Palauttaa line:n sis<69>lt<6C>mien mjonojen lukum<75><6D>r<EFBFBD>n.
# Mjonojen v<>liss?t<>ytyy olla v<>lily<6C>nti. # Mjonojen v<>liss?t<>ytyy olla v<>lily<6C>nti.

1
R/selvitaDigitFormat.R
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@ -1,7 +1,6 @@
#' @title Find out the Digit Format #' @title Find out the Digit Format
#' @param line the first line after the "pop" word from data in Genepop format. # @note Function clarified based on the line format whether the alleles of the data are given using 2 or 3 numbers. #' @param line the first line after the "pop" word from data in Genepop format. # @note Function clarified based on the line format whether the alleles of the data are given using 2 or 3 numbers.
#' @return df #' @return df
#' @export
selvitaDigitFormat <- function(line) { selvitaDigitFormat <- function(line) {
# line on ensimm<6D>inen pop-sanan j<>lkeinen rivi # line on ensimm<6D>inen pop-sanan j<>lkeinen rivi
# Genepop-formaatissa olevasta datasta. funktio selvitt<74><74> # Genepop-formaatissa olevasta datasta. funktio selvitt<74><74>

2
R/simulateAllFreqs.R
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@ -2,8 +2,6 @@
#' @description Lisää jokaista alleelia joka populaation joka lokukseen j1/noalle(j) verran. Näin saatuja counts:eja vastaavista Dirichlet-jakaumista simuloidaan arvot populaatioiden alleelifrekvensseille. #' @description Lisää jokaista alleelia joka populaation joka lokukseen j1/noalle(j) verran. Näin saatuja counts:eja vastaavista Dirichlet-jakaumista simuloidaan arvot populaatioiden alleelifrekvensseille.
#' Add each allele to each locus in each population by j 1 / noalle(j). The Dirichlet distributions corresponding to the counts thus obtained simulate values for the allele frequencies of the populations. #' Add each allele to each locus in each population by j 1 / noalle(j). The Dirichlet distributions corresponding to the counts thus obtained simulate values for the allele frequencies of the populations.
#' @param noalle noalle #' @param noalle noalle
#' @export
simulateAllFreqs <- function(noalle) { simulateAllFreqs <- function(noalle) {
if (isGlobalEmpty(COUNTS)) { if (isGlobalEmpty(COUNTS)) {
max_noalle <- 0 max_noalle <- 0

2
R/simulateIndividuals.R
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@ -6,8 +6,6 @@
#' @param allfreqs allfreqs #' @param allfreqs allfreqs
#' @param pop pop #' @param pop pop
#' @param missing_level missing_level #' @param missing_level missing_level
#' @export
simulateIndividuals <- function(n, rowsFromInd, allfreqs, pop, missing_level) { simulateIndividuals <- function(n, rowsFromInd, allfreqs, pop, missing_level) {
nloci <- size(allfreqs, 2) nloci <- size(allfreqs, 2)

2
R/simuloiAlleeli.R
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@ -5,8 +5,6 @@
#' @param allfreqs allfreqa #' @param allfreqs allfreqa
#' @param pop pop #' @param pop pop
#' @param loc loc #' @param loc loc
#' @export
simuloiAlleeli <- function(allfreqs, pop, loc) { simuloiAlleeli <- function(allfreqs, pop, loc) {
if (length(dim(allfreqs)) == 0) { if (length(dim(allfreqs)) == 0) {
freqs <- 1 freqs <- 1

1
R/suoritaMuutos.R
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@ -3,7 +3,6 @@
#' @param osuusTaulu Percentage table? #' @param osuusTaulu Percentage table?
#' @param osuus percentage? #' @param osuus percentage?
#' @param indeksi index #' @param indeksi index
#' @export
suoritaMuutos <- function(osuusTaulu, osuus, indeksi) { suoritaMuutos <- function(osuusTaulu, osuus, indeksi) {
if (isGlobalEmpty(COUNTS)) { if (isGlobalEmpty(COUNTS)) {
npops <- 1 npops <- 1

1
R/takeLine.R
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@ -3,7 +3,6 @@
#' @param description description #' @param description description
#' @param width width #' @param width width
#' @return newline #' @return newline
#' @export
takeLine <- function(description, width) { takeLine <- function(description, width) {
# Returns one line from the description: line ends to the first # Returns one line from the description: line ends to the first
# space after width:th mark. # space after width:th mark.

1
R/testaaKoordinaatit.R
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@ -3,7 +3,6 @@
#' @param coordinates coordinates #' @param coordinates coordinates
#' @param interactive prompt user for relevant questions during execution #' @param interactive prompt user for relevant questions during execution
#' @return a list of defectives ("viallinen") and coordinates #' @return a list of defectives ("viallinen") and coordinates
#' @export
testaaKoordinaatit <- function(ninds, coordinates, interactive = TRUE) { testaaKoordinaatit <- function(ninds, coordinates, interactive = TRUE) {
# Testaa onko koordinaatit kunnollisia. # Testaa onko koordinaatit kunnollisia.
# modified by Lu Cheng, 05.12.2012 # modified by Lu Cheng, 05.12.2012

1
R/testaaOnkoKunnollinenBapsData.R
View file

@ -2,7 +2,6 @@
#' @description Test if loaded BAPS data is proper #' @description Test if loaded BAPS data is proper
#' @param data dataset #' @param data dataset
#' @return ninds #' @return ninds
#' @export
testaaOnkoKunnollinenBapsData <- function(data) { testaaOnkoKunnollinenBapsData <- function(data) {
# Tarkastaa onko viimeisess?sarakkeessa kaikki # Tarkastaa onko viimeisess?sarakkeessa kaikki
# luvut 1,2,...,n johonkin n:<3A><>n asti. # luvut 1,2,...,n johonkin n:<3A><>n asti.

1
R/testaaPop.R
View file

@ -3,7 +3,6 @@
#' @param rivi Line #' @param rivi Line
#' @return pal = 1 if the line starts with one of the following #' @return pal = 1 if the line starts with one of the following
# letter combinations: Pop, pop, POP. In all others cases, pal = 0 # letter combinations: Pop, pop, POP. In all others cases, pal = 0
#' @export
testaaPop <- function(rivi) { testaaPop <- function(rivi) {
# pal=1, mik<69>li rivi alkaa jollain seuraavista # pal=1, mik<69>li rivi alkaa jollain seuraavista
# kirjainyhdistelmist? Pop, pop, POP. Kaikissa muissa # kirjainyhdistelmist? Pop, pop, POP. Kaikissa muissa

77
R/writeMixtureInfo.R
View file

@ -10,7 +10,6 @@
#' @param partitionSummary partitionSummary #' @param partitionSummary partitionSummary
#' @param popnames popnames #' @param popnames popnames
#' @param fixedK fixedK #' @param fixedK fixedK
#' @export
writeMixtureInfo <- function( writeMixtureInfo <- function(
logml, rowsFromInd, data, adjprior, priorTerm, outPutFile, inputFile, logml, rowsFromInd, data, adjprior, priorTerm, outPutFile, inputFile,
partitionSummary, popnames, fixedK partitionSummary, popnames, fixedK
@ -27,17 +26,18 @@ writeMixtureInfo <- function(
fid <- load(outPutFile) fid <- load(outPutFile)
} else { } else {
fid <- -1 fid <- -1
# TODO: replace sink with option that will record input and output outPutFile <- file.path(tempdir(), "baps4_output.baps")
sink("baps4_output.baps", split = TRUE) # save in text anyway. message("Output saved to", outPutFile)
sink(outPutFile, split = TRUE) # save in text anyway.
} }
dispLine() dispLine()
cat("RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:") cat("RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:\n")
cat(c("Data file: ", inputFile)) cat("Data file: ", inputFile, "\n")
cat("Model: independent") cat("Model: independent\n")
cat(c("Number of clustered individuals: ", ownNum2Str(ninds))) cat("Number of clustered individuals: ", ownNum2Str(ninds), "\n")
cat(c("Number of groups in optimal partition: ", ownNum2Str(npops))) cat("Number of groups in optimal partition: ", ownNum2Str(npops), "\n")
cat(c("Log(marginal likelihood) of optimal partition: ", ownNum2Str(logml))) cat("Log(marginal likelihood) of optimal partition: ", ownNum2Str(logml), "\n")
cat(" ") cat(" ")
if (fid != -1) { if (fid != -1) {
append(fid, "RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:\n") append(fid, "RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:\n")
@ -88,10 +88,10 @@ writeMixtureInfo <- function(
"Cluster ", as.character(m), ": {", as.character(indsInM[1]) "Cluster ", as.character(m), ": {", as.character(indsInM[1])
) )
for (k in 2:cluster_size) { for (k in 2:cluster_size) {
text <- c(text, ", ", as.character(indsInM[k])) text <- c(text, ",", as.character(indsInM[k]))
} }
} }
text <- c(text, "}") text <- c(text, "}\n")
while (length(text) > 58) { while (length(text) > 58) {
# Take one line and display it. # Take one line and display it.
new_line <- takeLine(text, 58) new_line <- takeLine(text, 58)
@ -107,7 +107,7 @@ writeMixtureInfo <- function(
text <- "" text <- ""
} }
} }
if (text != "") { if (any(text != "")) {
cat(text) cat(text)
if (fid != -1) { if (fid != -1) {
append(fid, text) append(fid, text)
@ -117,11 +117,11 @@ writeMixtureInfo <- function(
} }
if (npops > 1) { if (npops > 1) {
cat(" ") cat("\n")
cat(" ") cat("\n")
cat( cat(
"Changes in log(marginal likelihood)", "Changes in log(marginal likelihood)",
" if indvidual i is moved to group j:" " if indvidual i is moved to group j:\n"
) )
if (fid != -1) { if (fid != -1) {
append(fid, " ") append(fid, " ")
@ -132,7 +132,7 @@ writeMixtureInfo <- function(
fid, fid,
c( c(
"Changes in log(marginal likelihood)", "Changes in log(marginal likelihood)",
"if indvidual i is moved to group j:" "if indvidual i is moved to group j:\n"
) )
) )
append(fid, "\n") append(fid, "\n")
@ -168,9 +168,9 @@ writeMixtureInfo <- function(
if (names) { if (names) {
nimi <- as.character(popnames[ind]) nimi <- as.character(popnames[ind])
rivi <- c(blanks(maxSize - length(nimi)), nimi, ":") rivi <- c(blanks(maxSize - length(nimi)), nimi, ":\n")
} else { } else {
rivi <- c(blanks(4 - floor(log10(ind))), ownNum2Str(ind), ":") rivi <- c("\n", blanks(4 - floor(log10(ind))), ownNum2Str(ind), ":\n")
} }
for (j in 1:npops) { for (j in 1:npops) {
rivi <- c(rivi, " ", logml2String(omaRound(muutokset[j]))) rivi <- c(rivi, " ", logml2String(omaRound(muutokset[j])))
@ -182,9 +182,9 @@ writeMixtureInfo <- function(
} }
} }
cat(" ") cat("\n")
cat(" ") cat("\n")
cat("KL-divergence matrix in PHYLIP format:") cat("KL-divergence matrix in PHYLIP format:\n")
dist_mat <- zeros(npops, npops) dist_mat <- zeros(npops, npops)
if (fid != -1) { if (fid != -1) {
@ -194,6 +194,7 @@ writeMixtureInfo <- function(
append(fid, "\n") append(fid, "\n")
} }
COUNTS <- COUNTS[seq_len(nrow(adjprior)), seq_len(ncol(adjprior)), , drop = FALSE]
maxnoalle <- size(COUNTS, 1) maxnoalle <- size(COUNTS, 1)
nloci <- size(COUNTS, 2) nloci <- size(COUNTS, 2)
d <- zeros(maxnoalle, nloci, npops) d <- zeros(maxnoalle, nloci, npops)
@ -205,8 +206,8 @@ writeMixtureInfo <- function(
prior[1, nollia] <- 1 prior[1, nollia] <- 1
for (pop1 in 1:npops) { for (pop1 in 1:npops) {
d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) / squeezed_COUNTS_prior <- squeeze(COUNTS[, , pop1]) + prior
repmat(sum(squeeze(COUNTS[, , pop1]) + prior), c(maxnoalle, 1)) d[, , pop1] <- squeezed_COUNTS_prior / sum(squeezed_COUNTS_prior)
} }
ekarivi <- as.character(npops) ekarivi <- as.character(npops)
cat(ekarivi) cat(ekarivi)
@ -216,14 +217,14 @@ writeMixtureInfo <- function(
} }
for (pop1 in 1:npops) { for (pop1 in 1:npops) {
for (pop2 in 1:(pop1 - 1)) { for (pop2 in seq_len(pop1 - 1)) {
dist1 <- d[, , pop1] dist1 <- d[, , pop1]
dist2 <- d[, , pop2] dist2 <- d[, , pop2]
div12 <- sum( div12 <- sum(
sum(dist1 * log2((dist1 + 10^-10) / (dist2 + 10^-10))) sum(dist1 * base::log2((dist1 + 10^-10) / (dist2 + 10^-10)))
) / nloci ) / nloci
div21 <- sum( div21 <- sum(
sum(dist2 * log2((dist2 + 10^-10) / (dist1 + 10^-10))) sum(dist2 * base::log2((dist2 + 10^-10) / (dist1 + 10^-10)))
) / nloci ) / nloci
div <- (div12 + div21) / 2 div <- (div12 + div21) / 2
dist_mat[pop1, pop2] <- div dist_mat[pop1, pop2] <- div
@ -233,9 +234,9 @@ writeMixtureInfo <- function(
dist_mat <- dist_mat + t(dist_mat) # make it symmetric dist_mat <- dist_mat + t(dist_mat) # make it symmetric
for (pop1 in 1:npops) { for (pop1 in 1:npops) {
rivi <- c("Cluster_", as.character(pop1), " ") rivi <- c("\nCluster_", as.character(pop1), "\n")
for (pop2 in 1:npops) { for (pop2 in 1:npops) {
rivi <- c(rivi, kldiv2str(dist_mat[pop1, pop2]), " ") rivi <- c(rivi, kldiv2str(dist_mat[pop1, pop2]))
} }
cat(rivi) cat(rivi)
if (fid != -1) { if (fid != -1) {
@ -245,11 +246,11 @@ writeMixtureInfo <- function(
} }
} }
cat(" ") cat("\n")
cat(" ") cat("\n")
cat( cat(
"List of sizes of 10 best visited partitions", "List of sizes of 10 best visited partitions",
"and corresponding log(ml) values" "and corresponding log(ml) values\n"
) )
if (fid != -1) { if (fid != -1) {
@ -279,7 +280,7 @@ writeMixtureInfo <- function(
line <- c( line <- c(
as.character(partitionSummary[part, 1]), as.character(partitionSummary[part, 1]),
" ", " ",
as.character(partitionSummary(part, 2)) as.character(partitionSummary[part, 2])
) )
cat(line) cat(line)
if (fid != -1) { if (fid != -1) {
@ -289,9 +290,9 @@ writeMixtureInfo <- function(
} }
if (!fixedK) { if (!fixedK) {
cat(" ") cat("\n")
cat(" ") cat("\n")
cat("Probabilities for number of clusters") cat("Probabilities for number of clusters\n")
if (fid != -1) { if (fid != -1) {
append(fid, " ") append(fid, " ")
@ -323,7 +324,7 @@ writeMixtureInfo <- function(
line <- c( line <- c(
as.character(npopsTaulu[i]), " ", as.character(probs[i]) as.character(npopsTaulu[i]), " ", as.character(probs[i])
) )
cat(line) cat(line, "\n")
if (fid != -1) { if (fid != -1) {
append(fid, line) append(fid, line)
append(fid, "\n") append(fid, "\n")
@ -331,5 +332,9 @@ writeMixtureInfo <- function(
} }
} }
} }
# Closing sink(s)
while (sink.number() > 0L) {
sink()
}
return(changesInLogml) return(changesInLogml)
} }

1
R/writeMixtureInfoPop.R
View file

@ -10,7 +10,6 @@
#' @param partitionSummary partitionSummary #' @param partitionSummary partitionSummary
#' @param popnames popnames #' @param popnames popnames
#' @param fixedK fixedK #' @param fixedK fixedK
#' @export
writeMixtureInfoPop <- function(logml, rows, data, adjprior, priorTerm, writeMixtureInfoPop <- function(logml, rows, data, adjprior, priorTerm,
outPutFile, inputFile, partitionSummary, outPutFile, inputFile, partitionSummary,
popnames, fixedK) { popnames, fixedK) {

20
inst/ext/BAPS_format_clustering_diploid.txt → inst/extdata/BAPS_format_clustering_diploid.txt vendored
View file

@ -1,10 +1,10 @@
-9 102 56 80 100 90 118 90 88 104 1 -9 102 56 80 100 90 118 90 88 104 1
-9 102 54 82 102 92 116 90 86 104 1 -9 102 54 82 102 92 116 90 86 104 1
88 104 56 84 102 -9 120 90 88 100 2 88 104 56 84 102 -9 120 90 88 100 2
86 102 56 80 102 -9 116 90 86 100 2 86 102 56 80 102 -9 116 90 86 100 2
88 102 54 80 102 90 116 92 -9 100 3 88 102 54 80 102 90 116 92 -9 100 3
88 102 56 80 100 90 118 90 -9 104 3 88 102 56 80 100 90 118 90 -9 104 3
80 102 54 82 102 92 116 90 86 104 4 80 102 54 82 102 92 116 90 86 104 4
88 104 56 84 102 92 120 90 88 100 4 88 104 56 84 102 92 120 90 88 100 4
86 102 56 80 -9 90 116 90 86 100 5 86 102 56 80 -9 90 116 90 86 100 5
88 102 54 80 -9 90 116 92 86 100 5 88 102 54 80 -9 90 116 92 86 100 5

40
inst/ext/FASTA_clustering_haploid.fasta → inst/extdata/FASTA_clustering_haploid.fasta vendored
View file

@ -1,20 +1,20 @@
>1 >1
AACGAAACGATCGCGTCACCGGAACGTTGTCCGTCTCGAATAGCACTGTGGGAACGTGTTTTACATTCGT AACGAAACGATCGCGTCACCGGAACGTTGTCCGTCTCGAATAGCACTGTGGGAACGTGTTTTACATTCGT
TAGTAACATGGTCAGCTGCTCATCCGTATT TAGTAACATGGTCAGCTGCTCATCCGTATT
>2 >2
ATCAGCAAACGAGAAGTTGCAGAGGTCTTTGGTTTGAGCATTGCCCCCATACAATCGACTTCTGGCCTGG ATCAGCAAACGAGAAGTTGCAGAGGTCTTTGGTTTGAGCATTGCCCCCATACAATCGACTTCTGGCCTGG
AATGCACCACAAACATACCCCACAGGCTCG AATGCACCACAAACATACCCCACAGGCTCG
>3 >3
GCTTTTACTAAGGCCTATCGGATTCAACGTCACTAAGACTCGGCACTAACAGGCCGTTGTAAGCCGCTCT GCTTTTACTAAGGCCTATCGGATTCAACGTCACTAAGACTCGGCACTAACAGGCCGTTGTAAGCCGCTCT
GTCTGAGTATGGATGGTGGAGGCGGAGCCG GTCTGAGTATGGATGGTGGAGGCGGAGCCG
>4 >4
ACCTGGACCTCTGTATTAACGGCTGTGATTCTGAGGGGGGTATCGCAGCGCACTTTCTAGCTATATCACG ACCTGGACCTCTGTATTAACGGCTGTGATTCTGAGGGGGGTATCGCAGCGCACTTTCTAGCTATATCACG
CAAGGATAAAGTTCACCCATCACGTTGACC CAAGGATAAAGTTCACCCATCACGTTGACC
>5 >5
ACAATACGTCATCCACACCGCGCCTATGGAAGAATTTGCCCTTTCGGCGACAGCCCATGCTGTCAAGGAG ACAATACGTCATCCACACCGCGCCTATGGAAGAATTTGCCCTTTCGGCGACAGCCCATGCTGTCAAGGAG
GTAACATAGCTACCAGGTCCCATTCCAGGA GTAACATAGCTACCAGGTCCCATTCCAGGA

0
inst/ext/bam_example.bam → inst/extdata/bam_example.bam vendored
View file

0
inst/ext/sam_example.sam → inst/extdata/sam_example.sam vendored
View file

44290
inst/ext/seqs.fa → inst/extdata/seqs.fa vendored
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File diff suppressed because it is too large Load diff

0
inst/ext/vcf_example.vcf → inst/extdata/vcf_example.vcf vendored
View file

0
inst/ext/ExamplesDataFormatting/Example MLST DNA sequence data in concatenated Excel format.xls → inst/testdata/ExamplesDataFormatting/Example MLST DNA sequence data in concatenated Excel format.xls vendored
View file

216
inst/ext/ExamplesDataFormatting/Example baseline data in GENEPOP format for Trained clustering.txt → inst/testdata/ExamplesDataFormatting/Example baseline data in GENEPOP format for Trained clustering.txt vendored
View file

@ -1,108 +1,108 @@
Baseline data for trained clustering Baseline data for trained clustering
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
POP POP
Area1_1, 1717 1111 0000 0404 0505 0606 0707 1212 0707 1212 Area1_1, 1717 1111 0000 0404 0505 0606 0707 1212 0707 1212
Area1_2, 0909 1010 0505 0404 0505 1010 0707 1111 0707 1212 Area1_2, 0909 1010 0505 0404 0505 1010 0707 1111 0707 1212
Area1_3, 0000 1010 1414 0404 0505 0606 1111 1212 0707 1212 Area1_3, 0000 1010 1414 0404 0505 0606 1111 1212 0707 1212
Area1_4, 1111 1010 1515 0404 0101 0606 1313 1111 0707 0808 Area1_4, 1111 1010 1515 0404 0101 0606 1313 1111 0707 0808
Area1_5, 0101 1010 1212 0404 0505 0606 0909 1212 0707 1212 Area1_5, 0101 1010 1212 0404 0505 0606 0909 1212 0707 1212
Area1_6, 1111 1010 1414 0404 0505 0606 1111 1212 0707 1010 Area1_6, 1111 1010 1414 0404 0505 0606 1111 1212 0707 1010
Area1_7, 1515 1010 0707 0404 0505 0606 0707 1212 0707 1212 Area1_7, 1515 1010 0707 0404 0505 0606 0707 1212 0707 1212
Area1_8, 1515 1212 0606 0404 0505 0606 1616 1212 0707 1010 Area1_8, 1515 1212 0606 0404 0505 0606 1616 1212 0707 1010
Area1_9, 0909 1010 1010 0404 0505 0606 1919 1212 0707 1010 Area1_9, 0909 1010 1010 0404 0505 0606 1919 1212 0707 1010
Area1_10, 0000 0000 0000 0404 0505 0606 1111 1010 0707 0000 Area1_10, 0000 0000 0000 0404 0505 0606 1111 1010 0707 0000
Area1_11, 0000 0000 0000 0404 0505 0505 1919 1212 0404 1111 Area1_11, 0000 0000 0000 0404 0505 0505 1919 1212 0404 1111
Area1_12, 1515 1010 0606 0404 0505 0606 1616 1212 0202 1212 Area1_12, 1515 1010 0606 0404 0505 0606 1616 1212 0202 1212
Area1_13, 1515 1010 1515 0404 0505 0606 0707 0606 0707 1212 Area1_13, 1515 1010 1515 0404 0505 0606 0707 0606 0707 1212
Area1_14, 0505 1010 1919 0404 0202 0606 1111 1010 0202 1212 Area1_14, 0505 1010 1919 0404 0202 0606 1111 1010 0202 1212
Area1_15, 1111 1010 1414 0404 0505 0606 1616 0606 0707 1212 Area1_15, 1111 1010 1414 0404 0505 0606 1616 0606 0707 1212
Area1_16, 1515 1212 0000 0404 0505 0606 1919 1212 0707 1212 Area1_16, 1515 1212 0000 0404 0505 0606 1919 1212 0707 1212
Area1_17, 1515 1010 1515 0404 0505 0606 1919 1212 0707 1212 Area1_17, 1515 1010 1515 0404 0505 0606 1919 1212 0707 1212
Area1_18, 1515 1010 1212 0404 0505 0606 0909 1212 0707 1010 Area1_18, 1515 1010 1212 0404 0505 0606 0909 1212 0707 1010
Area1_19, 1515 1212 0606 0404 0505 0606 1515 1212 0707 1212 Area1_19, 1515 1212 0606 0404 0505 0606 1515 1212 0707 1212
Area1_20, 1717 1414 1717 0404 0505 0606 0808 1212 0707 0808 Area1_20, 1717 1414 1717 0404 0505 0606 0808 1212 0707 0808
Area1_21, 1515 1212 1212 0404 0505 0606 0808 1818 0707 1212 Area1_21, 1515 1212 1212 0404 0505 0606 0808 1818 0707 1212
Area1_22, 1313 1111 1212 0404 0505 0606 1313 1212 0707 1212 Area1_22, 1313 1111 1212 0404 0505 0606 1313 1212 0707 1212
Area1_23, 1515 1212 1212 0404 0505 0606 1313 1010 0707 1212 Area1_23, 1515 1212 1212 0404 0505 0606 1313 1010 0707 1212
Area1_24, 0000 0000 0000 0404 0505 0606 1919 1212 0404 0000 Area1_24, 0000 0000 0000 0404 0505 0606 1919 1212 0404 0000
Area1_25, 0000 0000 0000 0404 0202 0101 1919 1919 0707 1212 Area1_25, 0000 0000 0000 0404 0202 0101 1919 1919 0707 1212
Area1_26, 1313 1010 0202 0404 0505 0606 1717 1212 0707 1212 Area1_26, 1313 1010 0202 0404 0505 0606 1717 1212 0707 1212
POP POP
Area2_1, 1706 0505 1212 0404 0606 0808 1309 1111 0909 1413 Area2_1, 1706 0505 1212 0404 0606 0808 1309 1111 0909 1413
Area2_2, 1511 0707 1209 0404 0602 0808 1111 1004 0902 1310 Area2_2, 1511 0707 1209 0404 0602 0808 1111 1004 0902 1310
Area2_3, 1711 0000 1412 0402 0000 0000 1913 1002 0000 1313 Area2_3, 1711 0000 1412 0402 0000 0000 1913 1002 0000 1313
Area2_4, 1715 1515 1412 0404 0505 0604 1313 1010 1007 1309 Area2_4, 1715 1515 1412 0404 0505 0604 1313 1010 1007 1309
Area2_5, 1515 1515 1412 0404 0505 0606 1515 1111 0707 0908 Area2_5, 1515 1515 1412 0404 0505 0606 1515 1111 0707 0908
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Area2_8, 1307 0707 1212 0404 0606 0808 1313 1004 0909 1313 Area2_8, 1307 0707 1212 0404 0606 0808 1313 1004 0909 1313
Area2_9, 0000 1212 1209 0404 0505 0606 0000 1106 0707 1208 Area2_9, 0000 1212 1209 0404 0505 0606 0000 1106 0707 1208
Area2_10, 1307 0000 1212 0404 0606 0806 1313 1005 0909 1111 Area2_10, 1307 0000 1212 0404 0606 0806 1313 1005 0909 1111
Area2_11, 0000 0707 1409 0505 0606 0808 1616 1110 0909 1309 Area2_11, 0000 0707 1409 0505 0606 0808 1616 1110 0909 1309
Area2_12, 1807 0000 1212 0505 0606 0000 0909 0401 0909 1309 Area2_12, 1807 0000 1212 0505 0606 0000 0909 0401 0909 1309
Area2_13, 1511 1212 1212 0404 0505 0604 1313 1110 0707 1210 Area2_13, 1511 1212 1212 0404 0505 0604 1313 1110 0707 1210
Area2_14, 1111 1515 1412 0505 0606 0808 1414 1004 0909 1313 Area2_14, 1111 1515 1412 0505 0606 0808 1414 1004 0909 1313
Area2_15, 1817 0707 1212 0505 0707 0909 1111 1004 0909 1313 Area2_15, 1817 0707 1212 0505 0707 0909 1111 1004 0909 1313
Area2_16, 1913 1511 1212 0404 0606 0000 0909 1212 0909 1313 Area2_16, 1913 1511 1212 0404 0606 0000 0909 1212 0909 1313
Area2_17, 1515 0000 0000 0000 0505 1006 1313 1005 0707 1212 Area2_17, 1515 0000 0000 0000 0505 1006 1313 1005 0707 1212
Area2_18, 0707 0606 1408 0404 0202 0101 1313 1615 0202 1307 Area2_18, 0707 0606 1408 0404 0202 0101 1313 1615 0202 1307
Area2_19, 0707 1309 0909 0502 0202 0101 2009 1510 0202 0704 Area2_19, 0707 1309 0909 0502 0202 0101 2009 1510 0202 0704
POP POP
Area3_1, 1507 0706 1212 0202 0202 0000 0905 1409 0202 0707 Area3_1, 1507 0706 1212 0202 0202 0000 0905 1409 0202 0707
Area3_2, 1507 1313 1212 0202 0202 0101 1613 1510 0202 0807 Area3_2, 1507 1313 1212 0202 0202 0101 1613 1510 0202 0807
Area3_3, 1313 1414 1212 0404 0202 0101 1909 1510 0202 0704 Area3_3, 1313 1414 1212 0404 0202 0101 1909 1510 0202 0704
Area3_4, 1515 0909 1212 0502 0202 0101 1409 1210 0202 0807 Area3_4, 1515 0909 1212 0502 0202 0101 1409 1210 0202 0807
Area3_5, 1515 0808 1212 0502 0202 0101 1111 1510 0202 1007 Area3_5, 1515 0808 1212 0502 0202 0101 1111 1510 0202 1007
Area3_6, 1306 0909 1212 0202 0202 0101 0807 1512 0202 0707 Area3_6, 1306 0909 1212 0202 0202 0101 0807 1512 0202 0707
Area3_7, 0000 1009 1212 0404 0202 0101 1109 0702 0202 0808 Area3_7, 0000 1009 1212 0404 0202 0101 1109 0702 0202 0808
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Area3_9, 1515 0606 1212 0202 0202 0101 0909 1510 0202 1207 Area3_9, 1515 0606 1212 0202 0202 0101 0909 1510 0202 1207
Area3_10, 1307 1010 1412 0202 0202 0101 1709 1615 0202 1207 Area3_10, 1307 1010 1412 0202 0202 0101 1709 1615 0202 1207
Area3_11, 1307 1005 1212 0404 0202 0101 1709 1510 0202 0703 Area3_11, 1307 1005 1212 0404 0202 0101 1709 1510 0202 0703
Area3_12, 1109 0902 1212 0404 0202 0101 0909 1002 0202 1207 Area3_12, 1109 0902 1212 0404 0202 0101 0909 1002 0202 1207
Area3_13, 1307 0606 1412 0404 0202 0101 0807 1515 0202 1207 Area3_13, 1307 0606 1412 0404 0202 0101 0807 1515 0202 1207
Area3_14, 1717 1407 1212 0404 0202 0101 1107 1409 0202 0805 Area3_14, 1717 1407 1212 0404 0202 0101 1107 1409 0202 0805
Area3_15, 1307 1007 1412 0404 0505 0101 0909 0000 0202 0807 Area3_15, 1307 1007 1412 0404 0505 0101 0909 0000 0202 0807
Area3_16, 1811 0000 1212 0404 0505 0000 1515 0707 0000 1212 Area3_16, 1811 0000 1212 0404 0505 0000 1515 0707 0000 1212
Area3_17, 1907 1414 1512 0402 0705 0000 0000 0000 0909 1212 Area3_17, 1907 1414 1512 0402 0705 0000 0000 0000 0909 1212
POP POP
Area4_1, 1311 2019 1212 0404 0505 0000 1919 0707 0707 1109 Area4_1, 1311 2019 1212 0404 0505 0000 1919 0707 0707 1109
Area4_2, 1309 2018 1512 0404 0505 0000 1919 0808 0707 1111 Area4_2, 1309 2018 1512 0404 0505 0000 1919 0808 0707 1111
Area4_3, 1509 2118 1212 0404 0505 0000 1515 0707 0707 1107 Area4_3, 1509 2118 1212 0404 0505 0000 1515 0707 0707 1107
Area4_4, 1715 2221 1512 0404 0505 0000 1919 0707 0707 1111 Area4_4, 1715 2221 1512 0404 0505 0000 1919 0707 0707 1111
Area4_5, 1515 2121 1512 0404 0505 0000 1515 0707 0606 0707 Area4_5, 1515 2121 1512 0404 0505 0000 1515 0707 0606 0707
Area4_6, 1717 2222 1512 0404 0505 0000 1913 0707 0707 0907 Area4_6, 1717 2222 1512 0404 0505 0000 1913 0707 0707 0907
Area4_7, 1715 2221 1512 0404 0505 0000 1313 0707 0606 1111 Area4_7, 1715 2221 1512 0404 0505 0000 1313 0707 0606 1111
Area4_8, 1813 2320 1512 0404 0505 0000 1515 0707 0707 1107 Area4_8, 1813 2320 1512 0404 0505 0000 1515 0707 0707 1107
Area4_9, 1311 2019 1512 0404 0505 0000 1515 0707 0707 1111 Area4_9, 1311 2019 1512 0404 0505 0000 1515 0707 0707 1111
Area4_10, 1311 2019 1512 0000 0505 0000 1313 0707 0707 0909 Area4_10, 1311 2019 1512 0000 0505 0000 1313 0707 0707 0909
Area4_11, 1111 2625 0000 0000 0505 0606 0707 0903 0505 0505 Area4_11, 1111 2625 0000 0000 0505 0606 0707 0903 0505 0505
Area4_12, 0907 2724 0000 0404 0505 0605 1914 1105 0505 0707 Area4_12, 0907 2724 0000 0404 0505 0605 1914 1105 0505 0707
Area4_13, 1511 1610 1212 0404 0505 0605 1513 1105 1107 0704 Area4_13, 1511 1610 1212 0404 0505 0605 1513 1105 1107 0704
Area4_14, 1513 0404 1111 0404 0505 0606 1515 1111 1207 1107 Area4_14, 1513 0404 1111 0404 0505 0606 1515 1111 1207 1107
Area4_15, 1311 1616 1212 0404 0505 0606 1313 1111 0707 1107 Area4_15, 1311 1616 1212 0404 0505 0606 1313 1111 0707 1107
Area4_16, 1109 0606 1212 0404 0000 0000 1515 0902 0807 0505 Area4_16, 1109 0606 1212 0404 0000 0000 1515 0902 0807 0505
Area4_17, 1107 1004 1506 0404 0505 0606 1515 1212 0707 1108 Area4_17, 1107 1004 1506 0404 0505 0606 1515 1212 0707 1108
Area4_18, 1107 0904 1512 0404 0505 0606 1913 1105 1107 1107 Area4_18, 1107 0904 1512 0404 0505 0606 1913 1105 1107 1107
Area4_19, 1109 1313 1212 0404 0505 0606 1915 1111 1007 1107 Area4_19, 1109 1313 1212 0404 0505 0606 1915 1111 1007 1107
Area4_20, 1711 1604 1212 0404 0505 0606 1915 1212 1107 1007 Area4_20, 1711 1604 1212 0404 0505 0606 1915 1212 1107 1007
Area4_21, 1111 0606 1515 0404 0505 0606 1707 1009 0807 0502 Area4_21, 1111 0606 1515 0404 0505 0606 1707 1009 0807 0502
Area4_22, 1311 0603 1512 0404 0505 0606 1714 0707 0807 0501 Area4_22, 1311 0603 1512 0404 0505 0606 1714 0707 0807 0501
POP POP
Area5_1, 0711 1212 1513 0202 0707 0808 1408 0000 0909 1210 Area5_1, 0711 1212 1513 0202 0707 0808 1408 0000 0909 1210
Area5_2, 1118 0101 1212 0202 0803 0901 0808 0000 1101 1211 Area5_2, 1118 0101 1212 0202 0803 0901 0808 0000 1101 1211
Area5_3, 1518 0000 1512 0404 0707 0806 0909 0000 0909 1212 Area5_3, 1518 0000 1512 0404 0707 0806 0909 0000 0909 1212
Area5_4, 1309 0000 1512 0202 0606 0707 1508 0000 0808 1204 Area5_4, 1309 0000 1512 0202 0606 0707 1508 0000 0808 1204
Area5_5, 0718 0000 1512 0402 0707 0806 0707 0000 0909 1208 Area5_5, 0718 0000 1512 0402 0707 0806 0707 0000 0909 1208
Area5_6, 1818 1414 1212 0404 0707 0000 1916 0000 0909 1208 Area5_6, 1818 1414 1212 0404 0707 0000 1916 0000 0909 1208
Area5_7, 1318 1313 1212 0404 0606 0000 1908 0000 0808 1008 Area5_7, 1318 1313 1212 0404 0606 0000 1908 0000 0808 1008
Area5_8, 1818 0000 1212 0404 0000 0806 1616 0000 0808 1212 Area5_8, 1818 0000 1212 0404 0000 0806 1616 0000 0808 1212

8
inst/ext/ExamplesDataFormatting/Example coordinates for the spatial clustering of DNA sequences.txt → inst/testdata/ExamplesDataFormatting/Example coordinates for the spatial clustering of DNA sequences.txt vendored
View file

@ -1,5 +1,5 @@
3.1013610e+006 6.7104850e+006 3.1013610e+006 6.7104850e+006
3.1011410e+006 6.7108850e+006 3.1011410e+006 6.7108850e+006
3.1015790e+006 6.7101790e+006 3.1015790e+006 6.7101790e+006
3.1015910e+006 6.7100650e+006 3.1015910e+006 6.7100650e+006
3.1017660e+006 6.7104190e+006 3.1017660e+006 6.7104190e+006

20
inst/ext/ExamplesDataFormatting/Example coordinates for the spatial clustering.txt → inst/testdata/ExamplesDataFormatting/Example coordinates for the spatial clustering.txt vendored
View file

@ -1,10 +1,10 @@
3.1013610e+006 6.7104850e+006 3.1013610e+006 6.7104850e+006
3.1016310e+006 6.7101990e+006 3.1016310e+006 6.7101990e+006
3.1015790e+006 6.7101790e+006 3.1015790e+006 6.7101790e+006
3.1015910e+006 6.7100650e+006 3.1015910e+006 6.7100650e+006
3.1017660e+006 6.7104190e+006 3.1017660e+006 6.7104190e+006
3.1017500e+006 6.7106040e+006 3.1017500e+006 6.7106040e+006
3.1019250e+006 6.7092640e+006 3.1019250e+006 6.7092640e+006
3.1017430e+006 6.7094990e+006 3.1017430e+006 6.7094990e+006
3.1015240e+006 6.7097430e+006 3.1015240e+006 6.7097430e+006
0 0 0 0

20
inst/ext/ExamplesDataFormatting/Example data in BAPS format for clustering of diploid individuals.txt → inst/testdata/ExamplesDataFormatting/Example data in BAPS format for clustering of diploid individuals.txt vendored
View file

@ -1,10 +1,10 @@
-9 102 56 80 100 90 118 90 88 104 1 -9 102 56 80 100 90 118 90 88 104 1
-9 102 54 82 102 92 116 90 86 104 1 -9 102 54 82 102 92 116 90 86 104 1
88 104 56 84 102 -9 120 90 88 100 2 88 104 56 84 102 -9 120 90 88 100 2
86 102 56 80 102 -9 116 90 86 100 2 86 102 56 80 102 -9 116 90 86 100 2
88 102 54 80 102 90 116 92 -9 100 3 88 102 54 80 102 90 116 92 -9 100 3
88 102 56 80 100 90 118 90 -9 104 3 88 102 56 80 100 90 118 90 -9 104 3
80 102 54 82 102 92 116 90 86 104 4 80 102 54 82 102 92 116 90 86 104 4
88 104 56 84 102 92 120 90 88 100 4 88 104 56 84 102 92 120 90 88 100 4
86 102 56 80 -9 90 116 90 86 100 5 86 102 56 80 -9 90 116 90 86 100 5
88 102 54 80 -9 90 116 92 86 100 5 88 102 54 80 -9 90 116 92 86 100 5

10
inst/ext/ExamplesDataFormatting/Example data in BAPS format for clustering of haploid individuals.txt → inst/testdata/ExamplesDataFormatting/Example data in BAPS format for clustering of haploid individuals.txt vendored
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@ -1,5 +1,5 @@
88 102 56 80 100 90 118 -9 88 104 1 88 102 56 80 100 90 118 -9 88 104 1
80 102 54 82 102 92 116 90 86 104 2 80 102 54 82 102 92 116 90 86 104 2
88 104 56 84 102 -9 120 90 88 100 3 88 104 56 84 102 -9 120 90 88 100 3
86 102 56 80 102 90 116 90 86 100 4 86 102 56 80 102 90 116 90 86 100 4
88 102 -9 80 102 90 116 92 86 100 5 88 102 -9 80 102 90 116 92 86 100 5

40
inst/ext/ExamplesDataFormatting/Example data in BAPS format for clustering of tetraploid individuals.txt → inst/testdata/ExamplesDataFormatting/Example data in BAPS format for clustering of tetraploid individuals.txt vendored
View file

@ -1,20 +1,20 @@
88 102 56 80 100 90 118 90 88 104 1 88 102 56 80 100 90 118 90 88 104 1
80 102 54 82 102 92 116 90 86 104 1 80 102 54 82 102 92 116 90 86 104 1
88 102 56 80 100 90 118 90 88 104 1 88 102 56 80 100 90 118 90 88 104 1
80 102 54 82 102 92 116 90 86 104 1 80 102 54 82 102 92 116 90 86 104 1
88 -9 56 84 102 92 120 90 88 100 2 88 -9 56 84 102 92 120 90 88 100 2
86 -9 56 80 102 90 116 90 86 102 2 86 -9 56 80 102 90 116 90 86 102 2
88 -9 56 84 102 92 120 90 88 100 2 88 -9 56 84 102 92 120 90 88 100 2
86 -9 56 80 102 90 116 90 86 100 2 86 -9 56 80 102 90 116 90 86 100 2
88 102 54 82 102 90 116 92 86 102 3 88 102 54 82 102 90 116 92 86 102 3
88 102 56 80 100 90 118 90 88 104 3 88 102 56 80 100 90 118 90 88 104 3
88 102 54 80 102 90 116 92 86 102 3 88 102 54 80 102 90 116 92 86 102 3
88 102 56 80 100 90 118 90 88 104 3 88 102 56 80 100 90 118 90 88 104 3
80 102 54 82 102 92 116 -9 86 104 4 80 102 54 82 102 92 116 -9 86 104 4
88 104 56 84 102 92 120 -9 88 100 4 88 104 56 84 102 92 120 -9 88 100 4
80 102 54 82 102 92 116 -9 86 104 4 80 102 54 82 102 92 116 -9 86 104 4
88 104 56 84 102 92 120 -9 88 100 4 88 104 56 84 102 92 120 -9 88 100 4
86 102 56 80 102 90 116 90 86 100 5 86 102 56 80 102 90 116 90 86 100 5
88 104 54 80 102 90 116 92 86 100 5 88 104 54 80 102 90 116 92 86 100 5
86 102 56 80 104 90 116 90 86 100 5 86 102 56 80 104 90 116 90 86 100 5
88 102 54 80 102 90 116 92 86 100 5 88 102 54 80 102 90 116 92 86 100 5

20
inst/ext/ExamplesDataFormatting/Example data in BAPS format for group-wise clustering of diploid individuals.txt → inst/testdata/ExamplesDataFormatting/Example data in BAPS format for group-wise clustering of diploid individuals.txt vendored
View file

@ -1,10 +1,10 @@
-9 102 56 80 100 90 118 90 88 104 1 -9 102 56 80 100 90 118 90 88 104 1
-9 102 54 82 102 92 116 90 86 104 1 -9 102 54 82 102 92 116 90 86 104 1
88 104 56 84 102 -9 120 90 88 100 1 88 104 56 84 102 -9 120 90 88 100 1
86 102 56 80 102 -9 116 90 86 100 1 86 102 56 80 102 -9 116 90 86 100 1
88 102 54 80 102 90 116 92 -9 100 2 88 102 54 80 102 90 116 92 -9 100 2
88 102 56 80 100 90 118 90 -9 104 2 88 102 56 80 100 90 118 90 -9 104 2
80 102 54 82 102 92 116 90 86 104 2 80 102 54 82 102 92 116 90 86 104 2
88 104 56 84 102 92 120 90 88 100 2 88 104 56 84 102 92 120 90 88 100 2
86 102 56 80 -9 90 116 90 86 100 3 86 102 56 80 -9 90 116 90 86 100 3
88 102 54 80 -9 90 116 92 86 100 3 88 102 54 80 -9 90 116 92 86 100 3

10
inst/ext/ExamplesDataFormatting/Example data in BAPS format for group-wise clustering of haploid individuals.txt → inst/testdata/ExamplesDataFormatting/Example data in BAPS format for group-wise clustering of haploid individuals.txt vendored
View file

@ -1,5 +1,5 @@
88 102 56 80 100 90 118 -9 88 104 1 88 102 56 80 100 90 118 -9 88 104 1
80 102 54 82 102 92 116 90 86 104 1 80 102 54 82 102 92 116 90 86 104 1
88 104 56 84 102 -9 120 90 88 100 2 88 104 56 84 102 -9 120 90 88 100 2
86 102 56 80 102 90 116 90 86 100 2 86 102 56 80 102 90 116 90 86 100 2
88 102 -9 80 102 90 116 92 86 100 3 88 102 -9 80 102 90 116 92 86 100 3

40
inst/ext/ExamplesDataFormatting/Example data in BAPS format for group-wise clustering of tetraploid individuals.txt → inst/testdata/ExamplesDataFormatting/Example data in BAPS format for group-wise clustering of tetraploid individuals.txt vendored
View file

@ -1,20 +1,20 @@
88 102 56 80 100 90 118 90 88 104 1 88 102 56 80 100 90 118 90 88 104 1
80 102 54 82 102 92 116 90 86 104 1 80 102 54 82 102 92 116 90 86 104 1
88 102 56 80 100 90 118 90 88 104 1 88 102 56 80 100 90 118 90 88 104 1
80 102 54 82 102 92 116 90 86 104 1 80 102 54 82 102 92 116 90 86 104 1
88 -9 56 84 102 92 120 90 88 100 1 88 -9 56 84 102 92 120 90 88 100 1
86 -9 56 80 102 90 116 90 86 102 1 86 -9 56 80 102 90 116 90 86 102 1
88 -9 56 84 102 92 120 90 88 100 1 88 -9 56 84 102 92 120 90 88 100 1
86 -9 56 80 102 90 116 90 86 100 1 86 -9 56 80 102 90 116 90 86 100 1
88 102 54 82 102 90 116 92 86 102 2 88 102 54 82 102 90 116 92 86 102 2
88 102 56 80 100 90 118 90 88 104 2 88 102 56 80 100 90 118 90 88 104 2
88 102 54 80 102 90 116 92 86 102 2 88 102 54 80 102 90 116 92 86 102 2
88 102 56 80 100 90 118 90 88 104 2 88 102 56 80 100 90 118 90 88 104 2
80 102 54 82 102 92 116 -9 86 104 2 80 102 54 82 102 92 116 -9 86 104 2
88 104 56 84 102 92 120 -9 88 100 2 88 104 56 84 102 92 120 -9 88 100 2
80 102 54 82 102 92 116 -9 86 104 2 80 102 54 82 102 92 116 -9 86 104 2
88 104 56 84 102 92 120 -9 88 100 2 88 104 56 84 102 92 120 -9 88 100 2
86 102 56 80 102 90 116 90 86 100 3 86 102 56 80 102 90 116 90 86 100 3
88 104 54 80 102 90 116 92 86 100 3 88 104 54 80 102 90 116 92 86 100 3
86 102 56 80 104 90 116 90 86 100 3 86 102 56 80 104 90 116 90 86 100 3
88 102 54 80 102 90 116 92 86 100 3 88 102 54 80 102 90 116 92 86 100 3

40
inst/ext/ExamplesDataFormatting/Example data in FASTA format for clustering of haploid individuals.fasta → inst/testdata/ExamplesDataFormatting/Example data in FASTA format for clustering of haploid individuals.fasta vendored
View file

@ -1,20 +1,20 @@
>1 >1
AACGAAACGATCGCGTCACCGGAACGTTGTCCGTCTCGAATAGCACTGTGGGAACGTGTTTTACATTCGT AACGAAACGATCGCGTCACCGGAACGTTGTCCGTCTCGAATAGCACTGTGGGAACGTGTTTTACATTCGT
TAGTAACATGGTCAGCTGCTCATCCGTATT TAGTAACATGGTCAGCTGCTCATCCGTATT
>2 >2
ATCAGCAAACGAGAAGTTGCAGAGGTCTTTGGTTTGAGCATTGCCCCCATACAATCGACTTCTGGCCTGG ATCAGCAAACGAGAAGTTGCAGAGGTCTTTGGTTTGAGCATTGCCCCCATACAATCGACTTCTGGCCTGG
AATGCACCACAAACATACCCCACAGGCTCG AATGCACCACAAACATACCCCACAGGCTCG
>3 >3
GCTTTTACTAAGGCCTATCGGATTCAACGTCACTAAGACTCGGCACTAACAGGCCGTTGTAAGCCGCTCT GCTTTTACTAAGGCCTATCGGATTCAACGTCACTAAGACTCGGCACTAACAGGCCGTTGTAAGCCGCTCT
GTCTGAGTATGGATGGTGGAGGCGGAGCCG GTCTGAGTATGGATGGTGGAGGCGGAGCCG
>4 >4
ACCTGGACCTCTGTATTAACGGCTGTGATTCTGAGGGGGGTATCGCAGCGCACTTTCTAGCTATATCACG ACCTGGACCTCTGTATTAACGGCTGTGATTCTGAGGGGGGTATCGCAGCGCACTTTCTAGCTATATCACG
CAAGGATAAAGTTCACCCATCACGTTGACC CAAGGATAAAGTTCACCCATCACGTTGACC
>5 >5
ACAATACGTCATCCACACCGCGCCTATGGAAGAATTTGCCCTTTCGGCGACAGCCCATGCTGTCAAGGAG ACAATACGTCATCCACACCGCGCCTATGGAAGAATTTGCCCTTTCGGCGACAGCCCATGCTGTCAAGGAG
GTAACATAGCTACCAGGTCCCATTCCAGGA GTAACATAGCTACCAGGTCCCATTCCAGGA

16
inst/ext/ExamplesDataFormatting/Example of a BAPS formatted diploid sequence file for clustering with linked loci.txt → inst/testdata/ExamplesDataFormatting/Example of a BAPS formatted diploid sequence file for clustering with linked loci.txt vendored
View file

@ -1,8 +1,8 @@
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 1 CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 1
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 1 GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 1
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 2 GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 2
GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 2 GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 2
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 3 CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 3
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 3 GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 3
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 4 GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 4
GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 4 GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 4

8
inst/ext/ExamplesDataFormatting/Example of a BAPS formatted haploid sequence file for clustering with linked loci.txt → inst/testdata/ExamplesDataFormatting/Example of a BAPS formatted haploid sequence file for clustering with linked loci.txt vendored
View file

@ -1,4 +1,4 @@
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 1 CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 1
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 2 GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 2
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 3 CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 3
GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 4 GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 4

32
inst/ext/ExamplesDataFormatting/Example of a BAPS formatted tetraploid sequence file for clustering with linked loci.txt → inst/testdata/ExamplesDataFormatting/Example of a BAPS formatted tetraploid sequence file for clustering with linked loci.txt vendored
View file

@ -1,16 +1,16 @@
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 1 CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 1
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 1 GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 1
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 1 GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 1
GTTATTGACTCGGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 1 GTTATTGACTCGGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 1
TATCTAC--GAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 2 TATCTAC--GAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 2
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 2 GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 2
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 2 GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 2
GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 2 GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 2
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 3 CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 3
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 3 GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 3
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 3 GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 3
GTTATTGACTCGGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 3 GTTATTGACTCGGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 3
TATCTAC--GAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 4 TATCTAC--GAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 4
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 4 GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 4
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 4 GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 4
GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 4 GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 4

16
inst/ext/ExamplesDataFormatting/Example of a fasta formatted sequence file for gene recA.txt → inst/testdata/ExamplesDataFormatting/Example of a fasta formatted sequence file for gene recA.txt vendored
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@ -1,8 +1,8 @@
>RecA-1 >RecA-1
CTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAACCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC CTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAACCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC
>RecA-2 >RecA-2
CTAACGCTTGAGCTTATTGCTGCAGCACAGA-AGTGGGCAAAACGTGTGCATTCGTCGATGCCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC CTAACGCTTGAGCTTATTGCTGCAGCACAGA-AGTGGGCAAAACGTGTGCATTCGTCGATGCCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC
>RecA-3 >RecA-3
CTAACGC--GAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAACCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC CTAACGC--GAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAACCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC
>RecA-4 >RecA-4
CTAACGCTTGAGCTTATTGCTGCAGCACAGA-AGTGGGCAAAACGTGTGCATTCGTCGATGCCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC CTAACGCTTGAGCTTATTGCTGCAGCACAGA-AGTGGGCAAAACGTGTGCATTCGTCGATGCCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC

0
inst/ext/ExamplesDataFormatting/Example of a file specifying gene boundaries for a concatenated sequence in BAPS format.txt → inst/testdata/ExamplesDataFormatting/Example of a file specifying gene boundaries for a concatenated sequence in BAPS format.txt vendored
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20
inst/ext/ExamplesDataFormatting/Example of a partition file for admixture analysis based on user-specified populations.txt → inst/testdata/ExamplesDataFormatting/Example of a partition file for admixture analysis based on user-specified populations.txt vendored
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@ -1,10 +1,10 @@
1 1
1 1
2 2
2 2
2 2
2 2
2 2
3 3
3 3
3 3

10
inst/ext/ExamplesDataFormatting/Example of an MLST profile file for 6 genes.txt → inst/testdata/ExamplesDataFormatting/Example of an MLST profile file for 6 genes.txt vendored
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@ -1,5 +1,5 @@
ST Isolate Species Adk GyrB Hsp60 Mdh Pgi RecA ST Isolate Species Adk GyrB Hsp60 Mdh Pgi RecA
1 1A1 My. splendidone 1 1 1 1 1 1 1 1A1 My. splendidone 1 1 1 1 1 1
2 1B1 A. dent 2 2 2 2 2 2 2 1B1 A. dent 2 2 2 2 2 2
3 1B2 A. dent 3 3 3 3 3 3 3 1B2 A. dent 3 3 3 3 3 3
4 1A5 D. gently 4 4 4 4 4 4 4 1A5 D. gently 4 4 4 4 4 4

44290
inst/ext/ExamplesDataFormatting/Example of hierBAPS input in FASTA format.fa → inst/testdata/ExamplesDataFormatting/Example of hierBAPS input in FASTA format.fa vendored
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File diff suppressed because it is too large Load diff

0
inst/ext/ExamplesDataFormatting/Example of hierBAPS input in XLS format.xls → inst/testdata/ExamplesDataFormatting/Example of hierBAPS input in XLS format.xls vendored
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6
inst/ext/ExamplesDataFormatting/Example of sample population index file to be used with BAPS formatted data.txt → inst/testdata/ExamplesDataFormatting/Example of sample population index file to be used with BAPS formatted data.txt vendored
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@ -1,3 +1,3 @@
1 1
3 3
5 5

4
inst/ext/ExamplesDataFormatting/Example of sample population names file to be used with BAPS formatted data.txt → inst/testdata/ExamplesDataFormatting/Example of sample population names file to be used with BAPS formatted data.txt vendored
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@ -1,3 +1,3 @@
Example population 1 Example population 1
Example population 2 Example population 2
Example population 3 Example population 3

50
inst/ext/ExamplesDataFormatting/Example pre-grouped sample data in GENEPOP format for Trained clustering.txt → inst/testdata/ExamplesDataFormatting/Example pre-grouped sample data in GENEPOP format for Trained clustering.txt vendored
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@ -1,25 +1,25 @@
Sample data for trained clustering Sample data for trained clustering
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
POP POP
AreaUnknown_11, 1717 1010 1717 0404 0505 0606 0808 1010 0707 1212 AreaUnknown_11, 1717 1010 1717 0404 0505 0606 0808 1010 0707 1212
AreaUnknown_12, 1515 1212 1414 0404 0505 0606 1919 1010 0707 1414 AreaUnknown_12, 1515 1212 1414 0404 0505 0606 1919 1010 0707 1414
AreaUnknown_13, 1717 0303 1515 0404 0505 0606 1414 1010 0707 1212 AreaUnknown_13, 1717 0303 1515 0404 0505 0606 1414 1010 0707 1212
POP POP
AreaUnknown_24, 1311 1205 1212 0302 0505 0606 1313 1005 0707 1107 AreaUnknown_24, 1311 1205 1212 0302 0505 0606 1313 1005 0707 1107
AreaUnknown_25, 1306 0707 1212 0505 0603 0808 1109 1003 0000 0806 AreaUnknown_25, 1306 0707 1212 0505 0603 0808 1109 1003 0000 0806
POP POP
AreaUnknown_36, 0909 0606 1212 0404 0606 0808 1717 1001 0909 1308 AreaUnknown_36, 0909 0606 1212 0404 0606 0808 1717 1001 0909 1308
POP POP
AreaUnknown_47, 1507 1208 1212 0404 0202 0101 1915 1510 0202 0705 AreaUnknown_47, 1507 1208 1212 0404 0202 0101 1915 1510 0202 0705
AreaUnknown_48, 1507 1313 1212 0202 0202 0101 1414 1510 0202 0704 AreaUnknown_48, 1507 1313 1212 0202 0202 0101 1414 1510 0202 0704
AreaUnknown_49, 0907 0907 1212 0404 0202 0101 1908 0000 0202 1507 AreaUnknown_49, 0907 0907 1212 0404 0202 0101 1908 0000 0202 1507
AreaUnknown_410, 1109 1918 1512 0505 0505 0000 1605 0706 0707 0505 AreaUnknown_410, 1109 1918 1512 0505 0505 0000 1605 0706 0707 0505

62
inst/ext/ExamplesDataFormatting/Example sample data in GENEPOP format for Trained clustering.txt → inst/testdata/ExamplesDataFormatting/Example sample data in GENEPOP format for Trained clustering.txt vendored
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@ -1,31 +1,31 @@
Sample data for trained clustering Sample data for trained clustering
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
POP POP
AreaUnknown1, 1717 1010 1717 0404 0505 0606 0808 1010 0707 1212 AreaUnknown1, 1717 1010 1717 0404 0505 0606 0808 1010 0707 1212
POP POP
AreaUnknown2, 1515 1212 1414 0404 0505 0606 1919 1010 0707 1414 AreaUnknown2, 1515 1212 1414 0404 0505 0606 1919 1010 0707 1414
POP POP
AreaUnknown3, 1717 0303 1515 0404 0505 0606 1414 1010 0707 1212 AreaUnknown3, 1717 0303 1515 0404 0505 0606 1414 1010 0707 1212
POP POP
AreaUnknown4, 1311 1205 1212 0302 0505 0606 1313 1005 0707 1107 AreaUnknown4, 1311 1205 1212 0302 0505 0606 1313 1005 0707 1107
POP POP
AreaUnknown5, 1306 0707 1212 0505 0603 0808 1109 1003 0000 0806 AreaUnknown5, 1306 0707 1212 0505 0603 0808 1109 1003 0000 0806
POP POP
AreaUnknown6, 0909 0606 1212 0404 0606 0808 1717 1001 0909 1308 AreaUnknown6, 0909 0606 1212 0404 0606 0808 1717 1001 0909 1308
POP POP
AreaUnknown7, 1507 1208 1212 0404 0202 0101 1915 1510 0202 0705 AreaUnknown7, 1507 1208 1212 0404 0202 0101 1915 1510 0202 0705
POP POP
AreaUnknown8, 1507 1313 1212 0202 0202 0101 1414 1510 0202 0704 AreaUnknown8, 1507 1313 1212 0202 0202 0101 1414 1510 0202 0704
POP POP
AreaUnknown9, 0907 0907 1212 0404 0202 0101 1908 0000 0202 1507 AreaUnknown9, 0907 0907 1212 0404 0202 0101 1908 0000 0202 1507
POP POP
AreaUnknown10, 1109 1918 1512 0505 0505 0000 1605 0706 0707 0505 AreaUnknown10, 1109 1918 1512 0505 0505 0000 1605 0706 0707 0505

0
inst/ext/ExamplesDataFormatting/Example test data in XLS format for Trained clustering.xls → inst/testdata/ExamplesDataFormatting/Example test data in XLS format for Trained clustering.xls vendored
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0
inst/ext/ExamplesDataFormatting/Example training data in XLS format for Trained clustering.xls → inst/testdata/ExamplesDataFormatting/Example training data in XLS format for Trained clustering.xls vendored
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0
inst/ext/ExamplesDataFormatting/Notes.md → inst/testdata/ExamplesDataFormatting/Notes.md vendored
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216
inst/ext/GENEPOP_format_trained_clustering.gen → inst/testdata/datasets-for-future-releases/GENEPOP_format_trained_clustering.gen vendored
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@ -1,108 +1,108 @@
Baseline data for trained clustering Baseline data for trained clustering
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
POP POP
Area1_1, 1717 1111 0000 0404 0505 0606 0707 1212 0707 1212 Area1_1, 1717 1111 0000 0404 0505 0606 0707 1212 0707 1212
Area1_2, 0909 1010 0505 0404 0505 1010 0707 1111 0707 1212 Area1_2, 0909 1010 0505 0404 0505 1010 0707 1111 0707 1212
Area1_3, 0000 1010 1414 0404 0505 0606 1111 1212 0707 1212 Area1_3, 0000 1010 1414 0404 0505 0606 1111 1212 0707 1212
Area1_4, 1111 1010 1515 0404 0101 0606 1313 1111 0707 0808 Area1_4, 1111 1010 1515 0404 0101 0606 1313 1111 0707 0808
Area1_5, 0101 1010 1212 0404 0505 0606 0909 1212 0707 1212 Area1_5, 0101 1010 1212 0404 0505 0606 0909 1212 0707 1212
Area1_6, 1111 1010 1414 0404 0505 0606 1111 1212 0707 1010 Area1_6, 1111 1010 1414 0404 0505 0606 1111 1212 0707 1010
Area1_7, 1515 1010 0707 0404 0505 0606 0707 1212 0707 1212 Area1_7, 1515 1010 0707 0404 0505 0606 0707 1212 0707 1212
Area1_8, 1515 1212 0606 0404 0505 0606 1616 1212 0707 1010 Area1_8, 1515 1212 0606 0404 0505 0606 1616 1212 0707 1010
Area1_9, 0909 1010 1010 0404 0505 0606 1919 1212 0707 1010 Area1_9, 0909 1010 1010 0404 0505 0606 1919 1212 0707 1010
Area1_10, 0000 0000 0000 0404 0505 0606 1111 1010 0707 0000 Area1_10, 0000 0000 0000 0404 0505 0606 1111 1010 0707 0000
Area1_11, 0000 0000 0000 0404 0505 0505 1919 1212 0404 1111 Area1_11, 0000 0000 0000 0404 0505 0505 1919 1212 0404 1111
Area1_12, 1515 1010 0606 0404 0505 0606 1616 1212 0202 1212 Area1_12, 1515 1010 0606 0404 0505 0606 1616 1212 0202 1212
Area1_13, 1515 1010 1515 0404 0505 0606 0707 0606 0707 1212 Area1_13, 1515 1010 1515 0404 0505 0606 0707 0606 0707 1212
Area1_14, 0505 1010 1919 0404 0202 0606 1111 1010 0202 1212 Area1_14, 0505 1010 1919 0404 0202 0606 1111 1010 0202 1212
Area1_15, 1111 1010 1414 0404 0505 0606 1616 0606 0707 1212 Area1_15, 1111 1010 1414 0404 0505 0606 1616 0606 0707 1212
Area1_16, 1515 1212 0000 0404 0505 0606 1919 1212 0707 1212 Area1_16, 1515 1212 0000 0404 0505 0606 1919 1212 0707 1212
Area1_17, 1515 1010 1515 0404 0505 0606 1919 1212 0707 1212 Area1_17, 1515 1010 1515 0404 0505 0606 1919 1212 0707 1212
Area1_18, 1515 1010 1212 0404 0505 0606 0909 1212 0707 1010 Area1_18, 1515 1010 1212 0404 0505 0606 0909 1212 0707 1010
Area1_19, 1515 1212 0606 0404 0505 0606 1515 1212 0707 1212 Area1_19, 1515 1212 0606 0404 0505 0606 1515 1212 0707 1212
Area1_20, 1717 1414 1717 0404 0505 0606 0808 1212 0707 0808 Area1_20, 1717 1414 1717 0404 0505 0606 0808 1212 0707 0808
Area1_21, 1515 1212 1212 0404 0505 0606 0808 1818 0707 1212 Area1_21, 1515 1212 1212 0404 0505 0606 0808 1818 0707 1212
Area1_22, 1313 1111 1212 0404 0505 0606 1313 1212 0707 1212 Area1_22, 1313 1111 1212 0404 0505 0606 1313 1212 0707 1212
Area1_23, 1515 1212 1212 0404 0505 0606 1313 1010 0707 1212 Area1_23, 1515 1212 1212 0404 0505 0606 1313 1010 0707 1212
Area1_24, 0000 0000 0000 0404 0505 0606 1919 1212 0404 0000 Area1_24, 0000 0000 0000 0404 0505 0606 1919 1212 0404 0000
Area1_25, 0000 0000 0000 0404 0202 0101 1919 1919 0707 1212 Area1_25, 0000 0000 0000 0404 0202 0101 1919 1919 0707 1212
Area1_26, 1313 1010 0202 0404 0505 0606 1717 1212 0707 1212 Area1_26, 1313 1010 0202 0404 0505 0606 1717 1212 0707 1212
POP POP
Area2_1, 1706 0505 1212 0404 0606 0808 1309 1111 0909 1413 Area2_1, 1706 0505 1212 0404 0606 0808 1309 1111 0909 1413
Area2_2, 1511 0707 1209 0404 0602 0808 1111 1004 0902 1310 Area2_2, 1511 0707 1209 0404 0602 0808 1111 1004 0902 1310
Area2_3, 1711 0000 1412 0402 0000 0000 1913 1002 0000 1313 Area2_3, 1711 0000 1412 0402 0000 0000 1913 1002 0000 1313
Area2_4, 1715 1515 1412 0404 0505 0604 1313 1010 1007 1309 Area2_4, 1715 1515 1412 0404 0505 0604 1313 1010 1007 1309
Area2_5, 1515 1515 1412 0404 0505 0606 1515 1111 0707 0908 Area2_5, 1515 1515 1412 0404 0505 0606 1515 1111 0707 0908
Area2_6, 1309 0000 1212 0404 0603 0802 1311 1204 0903 1309 Area2_6, 1309 0000 1212 0404 0603 0802 1311 1204 0903 1309
Area2_7, 1313 0505 1212 0404 0606 0808 1616 1414 0909 1206 Area2_7, 1313 0505 1212 0404 0606 0808 1616 1414 0909 1206
Area2_8, 1307 0707 1212 0404 0606 0808 1313 1004 0909 1313 Area2_8, 1307 0707 1212 0404 0606 0808 1313 1004 0909 1313
Area2_9, 0000 1212 1209 0404 0505 0606 0000 1106 0707 1208 Area2_9, 0000 1212 1209 0404 0505 0606 0000 1106 0707 1208
Area2_10, 1307 0000 1212 0404 0606 0806 1313 1005 0909 1111 Area2_10, 1307 0000 1212 0404 0606 0806 1313 1005 0909 1111
Area2_11, 0000 0707 1409 0505 0606 0808 1616 1110 0909 1309 Area2_11, 0000 0707 1409 0505 0606 0808 1616 1110 0909 1309
Area2_12, 1807 0000 1212 0505 0606 0000 0909 0401 0909 1309 Area2_12, 1807 0000 1212 0505 0606 0000 0909 0401 0909 1309
Area2_13, 1511 1212 1212 0404 0505 0604 1313 1110 0707 1210 Area2_13, 1511 1212 1212 0404 0505 0604 1313 1110 0707 1210
Area2_14, 1111 1515 1412 0505 0606 0808 1414 1004 0909 1313 Area2_14, 1111 1515 1412 0505 0606 0808 1414 1004 0909 1313
Area2_15, 1817 0707 1212 0505 0707 0909 1111 1004 0909 1313 Area2_15, 1817 0707 1212 0505 0707 0909 1111 1004 0909 1313
Area2_16, 1913 1511 1212 0404 0606 0000 0909 1212 0909 1313 Area2_16, 1913 1511 1212 0404 0606 0000 0909 1212 0909 1313
Area2_17, 1515 0000 0000 0000 0505 1006 1313 1005 0707 1212 Area2_17, 1515 0000 0000 0000 0505 1006 1313 1005 0707 1212
Area2_18, 0707 0606 1408 0404 0202 0101 1313 1615 0202 1307 Area2_18, 0707 0606 1408 0404 0202 0101 1313 1615 0202 1307
Area2_19, 0707 1309 0909 0502 0202 0101 2009 1510 0202 0704 Area2_19, 0707 1309 0909 0502 0202 0101 2009 1510 0202 0704
POP POP
Area3_1, 1507 0706 1212 0202 0202 0000 0905 1409 0202 0707 Area3_1, 1507 0706 1212 0202 0202 0000 0905 1409 0202 0707
Area3_2, 1507 1313 1212 0202 0202 0101 1613 1510 0202 0807 Area3_2, 1507 1313 1212 0202 0202 0101 1613 1510 0202 0807
Area3_3, 1313 1414 1212 0404 0202 0101 1909 1510 0202 0704 Area3_3, 1313 1414 1212 0404 0202 0101 1909 1510 0202 0704
Area3_4, 1515 0909 1212 0502 0202 0101 1409 1210 0202 0807 Area3_4, 1515 0909 1212 0502 0202 0101 1409 1210 0202 0807
Area3_5, 1515 0808 1212 0502 0202 0101 1111 1510 0202 1007 Area3_5, 1515 0808 1212 0502 0202 0101 1111 1510 0202 1007
Area3_6, 1306 0909 1212 0202 0202 0101 0807 1512 0202 0707 Area3_6, 1306 0909 1212 0202 0202 0101 0807 1512 0202 0707
Area3_7, 0000 1009 1212 0404 0202 0101 1109 0702 0202 0808 Area3_7, 0000 1009 1212 0404 0202 0101 1109 0702 0202 0808
Area3_8, 1507 0606 1212 0404 0202 0101 1908 1409 0202 0707 Area3_8, 1507 0606 1212 0404 0202 0101 1908 1409 0202 0707
Area3_9, 1515 0606 1212 0202 0202 0101 0909 1510 0202 1207 Area3_9, 1515 0606 1212 0202 0202 0101 0909 1510 0202 1207
Area3_10, 1307 1010 1412 0202 0202 0101 1709 1615 0202 1207 Area3_10, 1307 1010 1412 0202 0202 0101 1709 1615 0202 1207
Area3_11, 1307 1005 1212 0404 0202 0101 1709 1510 0202 0703 Area3_11, 1307 1005 1212 0404 0202 0101 1709 1510 0202 0703
Area3_12, 1109 0902 1212 0404 0202 0101 0909 1002 0202 1207 Area3_12, 1109 0902 1212 0404 0202 0101 0909 1002 0202 1207
Area3_13, 1307 0606 1412 0404 0202 0101 0807 1515 0202 1207 Area3_13, 1307 0606 1412 0404 0202 0101 0807 1515 0202 1207
Area3_14, 1717 1407 1212 0404 0202 0101 1107 1409 0202 0805 Area3_14, 1717 1407 1212 0404 0202 0101 1107 1409 0202 0805
Area3_15, 1307 1007 1412 0404 0505 0101 0909 0000 0202 0807 Area3_15, 1307 1007 1412 0404 0505 0101 0909 0000 0202 0807
Area3_16, 1811 0000 1212 0404 0505 0000 1515 0707 0000 1212 Area3_16, 1811 0000 1212 0404 0505 0000 1515 0707 0000 1212
Area3_17, 1907 1414 1512 0402 0705 0000 0000 0000 0909 1212 Area3_17, 1907 1414 1512 0402 0705 0000 0000 0000 0909 1212
POP POP
Area4_1, 1311 2019 1212 0404 0505 0000 1919 0707 0707 1109 Area4_1, 1311 2019 1212 0404 0505 0000 1919 0707 0707 1109
Area4_2, 1309 2018 1512 0404 0505 0000 1919 0808 0707 1111 Area4_2, 1309 2018 1512 0404 0505 0000 1919 0808 0707 1111
Area4_3, 1509 2118 1212 0404 0505 0000 1515 0707 0707 1107 Area4_3, 1509 2118 1212 0404 0505 0000 1515 0707 0707 1107
Area4_4, 1715 2221 1512 0404 0505 0000 1919 0707 0707 1111 Area4_4, 1715 2221 1512 0404 0505 0000 1919 0707 0707 1111
Area4_5, 1515 2121 1512 0404 0505 0000 1515 0707 0606 0707 Area4_5, 1515 2121 1512 0404 0505 0000 1515 0707 0606 0707
Area4_6, 1717 2222 1512 0404 0505 0000 1913 0707 0707 0907 Area4_6, 1717 2222 1512 0404 0505 0000 1913 0707 0707 0907
Area4_7, 1715 2221 1512 0404 0505 0000 1313 0707 0606 1111 Area4_7, 1715 2221 1512 0404 0505 0000 1313 0707 0606 1111
Area4_8, 1813 2320 1512 0404 0505 0000 1515 0707 0707 1107 Area4_8, 1813 2320 1512 0404 0505 0000 1515 0707 0707 1107
Area4_9, 1311 2019 1512 0404 0505 0000 1515 0707 0707 1111 Area4_9, 1311 2019 1512 0404 0505 0000 1515 0707 0707 1111
Area4_10, 1311 2019 1512 0000 0505 0000 1313 0707 0707 0909 Area4_10, 1311 2019 1512 0000 0505 0000 1313 0707 0707 0909
Area4_11, 1111 2625 0000 0000 0505 0606 0707 0903 0505 0505 Area4_11, 1111 2625 0000 0000 0505 0606 0707 0903 0505 0505
Area4_12, 0907 2724 0000 0404 0505 0605 1914 1105 0505 0707 Area4_12, 0907 2724 0000 0404 0505 0605 1914 1105 0505 0707
Area4_13, 1511 1610 1212 0404 0505 0605 1513 1105 1107 0704 Area4_13, 1511 1610 1212 0404 0505 0605 1513 1105 1107 0704
Area4_14, 1513 0404 1111 0404 0505 0606 1515 1111 1207 1107 Area4_14, 1513 0404 1111 0404 0505 0606 1515 1111 1207 1107
Area4_15, 1311 1616 1212 0404 0505 0606 1313 1111 0707 1107 Area4_15, 1311 1616 1212 0404 0505 0606 1313 1111 0707 1107
Area4_16, 1109 0606 1212 0404 0000 0000 1515 0902 0807 0505 Area4_16, 1109 0606 1212 0404 0000 0000 1515 0902 0807 0505
Area4_17, 1107 1004 1506 0404 0505 0606 1515 1212 0707 1108 Area4_17, 1107 1004 1506 0404 0505 0606 1515 1212 0707 1108
Area4_18, 1107 0904 1512 0404 0505 0606 1913 1105 1107 1107 Area4_18, 1107 0904 1512 0404 0505 0606 1913 1105 1107 1107
Area4_19, 1109 1313 1212 0404 0505 0606 1915 1111 1007 1107 Area4_19, 1109 1313 1212 0404 0505 0606 1915 1111 1007 1107
Area4_20, 1711 1604 1212 0404 0505 0606 1915 1212 1107 1007 Area4_20, 1711 1604 1212 0404 0505 0606 1915 1212 1107 1007
Area4_21, 1111 0606 1515 0404 0505 0606 1707 1009 0807 0502 Area4_21, 1111 0606 1515 0404 0505 0606 1707 1009 0807 0502
Area4_22, 1311 0603 1512 0404 0505 0606 1714 0707 0807 0501 Area4_22, 1311 0603 1512 0404 0505 0606 1714 0707 0807 0501
POP POP
Area5_1, 0711 1212 1513 0202 0707 0808 1408 0000 0909 1210 Area5_1, 0711 1212 1513 0202 0707 0808 1408 0000 0909 1210
Area5_2, 1118 0101 1212 0202 0803 0901 0808 0000 1101 1211 Area5_2, 1118 0101 1212 0202 0803 0901 0808 0000 1101 1211
Area5_3, 1518 0000 1512 0404 0707 0806 0909 0000 0909 1212 Area5_3, 1518 0000 1512 0404 0707 0806 0909 0000 0909 1212
Area5_4, 1309 0000 1512 0202 0606 0707 1508 0000 0808 1204 Area5_4, 1309 0000 1512 0202 0606 0707 1508 0000 0808 1204
Area5_5, 0718 0000 1512 0402 0707 0806 0707 0000 0909 1208 Area5_5, 0718 0000 1512 0402 0707 0806 0707 0000 0909 1208
Area5_6, 1818 1414 1212 0404 0707 0000 1916 0000 0909 1208 Area5_6, 1818 1414 1212 0404 0707 0000 1916 0000 0909 1208
Area5_7, 1318 1313 1212 0404 0606 0000 1908 0000 0808 1008 Area5_7, 1318 1313 1212 0404 0606 0000 1908 0000 0808 1008
Area5_8, 1818 0000 1212 0404 0000 0806 1616 0000 0808 1212 Area5_8, 1818 0000 1212 0404 0000 0806 1616 0000 0808 1212

0
inst/ext/example_drosophila.Aligned.out.sam → inst/testdata/datasets-for-future-releases/example_drosophila.Aligned.out.sam vendored
View file

40
man/greedyMix.Rd
View file

@ -4,18 +4,56 @@
\alias{greedyMix} \alias{greedyMix}
\title{Clustering of individuals} \title{Clustering of individuals}
\usage{ \usage{
greedyMix(data, format, verbose = TRUE) greedyMix(
data,
format,
partitionCompare = NULL,
ninds = 1L,
npops = 1L,
counts = NULL,
sumcounts = NULL,
max_iter = 100L,
alleleCodes = NULL,
inp = NULL,
popnames = NULL,
fixedK = FALSE,
verbose = FALSE
)
} }
\arguments{ \arguments{
\item{data}{data file} \item{data}{data file}
\item{format}{Data format. Format supported: "FASTA", "VCF" ,"BAM", "GenePop"} \item{format}{Data format. Format supported: "FASTA", "VCF" ,"BAM", "GenePop"}
\item{partitionCompare}{a list of partitions to compare}
\item{ninds}{number of individuals}
\item{npops}{number of populations}
\item{counts}{counts}
\item{sumcounts}{sumcounts}
\item{max_iter}{maximum number of iterations}
\item{alleleCodes}{allele codes}
\item{inp}{input file}
\item{popnames}{population names}
\item{fixedK}{if \code{TRUE}, the number of populations is fixed}
\item{verbose}{if \code{TRUE}, prints extra output information} \item{verbose}{if \code{TRUE}, prints extra output information}
} }
\description{ \description{
Clustering of individuals Clustering of individuals
} }
\examples{
data <- system.file("extdata", "FASTA_clustering_haploid.fasta", package = "rBAPS")
greedyMix(data, "fasta")
}
\references{ \references{
Samtools: a suite of programs for interacting Samtools: a suite of programs for interacting
with high-throughput sequencing data. <http://www.htslib.org/> with high-throughput sequencing data. <http://www.htslib.org/>

8
man/handleData.Rd
View file

@ -4,10 +4,12 @@
\alias{handleData} \alias{handleData}
\title{Handle Data} \title{Handle Data}
\usage{ \usage{
handleData(raw_data) handleData(raw_data, format = "Genepop")
} }
\arguments{ \arguments{
\item{raw_data}{Raw data} \item{raw_data}{Raw data in Genepop or BAPS format}
\item{format}{data format}
} }
\description{ \description{
Handle Data Handle Data
@ -20,5 +22,5 @@ After this function. Add blank lines for individuals with fewer rows as
maximum. If the code of an allele is = 0, the function changes that allele maximum. If the code of an allele is = 0, the function changes that allele
code to the smallest code that is larger than any code in use. After this, code to the smallest code that is larger than any code in use. After this,
the function changes the allele codes so that one locus j the function changes the allele codes so that one locus j
codes get values between? 1, ..., Noah (j). codes get values between? 1, ..., noalle(j).
} }

26
man/importFile.Rd Normal file
View file

@ -0,0 +1,26 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/importFile.R
\name{importFile}
\alias{importFile}
\title{Import data file}
\usage{
importFile(data, format, verbose)
}
\arguments{
\item{data}{raw dataset}
\item{format}{data format (guesses from extension if not provided)}
\item{verbose}{if \code{TRUE}, prints extra output information}
}
\value{
The data in a format that can be used by the other functions
}
\description{
Imports data from several formats (FASTA, VCF, SAM, BAM,
Genepop).
}
\examples{
path_inst <- system.file("extdata", "", package = "rBAPS")
importFile(file.path(path_inst, "FASTA_clustering_haploid.fasta"))
}

11
man/load_fasta.Rd
View file

@ -4,12 +4,15 @@
\alias{load_fasta} \alias{load_fasta}
\title{load_fasta} \title{load_fasta}
\usage{ \usage{
load_fasta(msa, keep.singletons = FALSE) load_fasta(msa, keep_singletons = FALSE, output_numbers = TRUE)
} }
\arguments{ \arguments{
\item{msa}{Either the location of a fasta file or ape DNAbin object containing the multiple sequence alignment data to be clustered} \item{msa}{Either the location of a fasta file or ape DNAbin object containing the multiple sequence alignment data to be clustered}
\item{keep.singletons}{A logical indicating whether to consider singleton mutations in calculating the clusters} \item{keep_singletons}{A logical indicating whether to consider singleton mutations in calculating the clusters}
\item{output_numbers}{A logical indicating whether to output the data as
numbers (TRUE) or letters (FALSE)}
} }
\value{ \value{
A character matrix with filtered SNP data A character matrix with filtered SNP data
@ -19,8 +22,8 @@ Loads a fasta file into matrix format ready for
running the hierBAPS algorithm. running the hierBAPS algorithm.
} }
\examples{ \examples{
msa <- system.file("ext", "seqs.fa", package = "rBAPS") msa <- system.file("extdata", "seqs.fa", package = "rBAPS")
snp.matrix <- load_fasta(msa) snp.matrix <- rBAPS:::load_fasta(msa)
} }
\seealso{ \seealso{
rhierbaps::load_fasta rhierbaps::load_fasta

2
man/randdir.Rd
View file

@ -18,7 +18,7 @@ vector of length `nc` with r.v. realizations from Gamma(rate=1)
Generates random numbers Generates random numbers
} }
\examples{ \examples{
randdir(matrix(c(10, 30, 60), 3), 3) rBAPS:::randdir(matrix(c(10, 30, 60), 3), 3)
} }
\seealso{ \seealso{
randga randga

70
matlab/independent/indMix.m
View file

@ -87,7 +87,7 @@ for run = 1:nruns
apu = rows(i); apu = rows(i);
PARTITION(i) = initialPartition(apu(1)); PARTITION(i) = initialPartition(apu(1));
end end
COUNTS = counts; SUMCOUNTS = sumcounts; COUNTS = counts; SUMCOUNTS = sumcounts;
POP_LOGML = computePopulationLogml(1:npops, adjprior, priorTerm); POP_LOGML = computePopulationLogml(1:npops, adjprior, priorTerm);
LOGDIFF = repmat(-Inf,ninds,npops); LOGDIFF = repmat(-Inf,ninds,npops);
@ -98,7 +98,7 @@ for run = 1:nruns
kokeiltu = zeros(nRoundTypes, 1); kokeiltu = zeros(nRoundTypes, 1);
roundTypes = [1 1]; %Ykkösvaiheen sykli kahteen kertaan. roundTypes = [1 1]; %Ykkösvaiheen sykli kahteen kertaan.
ready = 0; vaihe = 1; ready = 0; vaihe = 1;
if dispText if dispText
disp(' '); disp(' ');
disp(['Mixture analysis started with initial ' num2str(npops) ' populations.']); disp(['Mixture analysis started with initial ' num2str(npops) ' populations.']);
@ -106,11 +106,11 @@ for run = 1:nruns
while ready ~= 1 while ready ~= 1
muutoksia = 0; muutoksia = 0;
if dispText if dispText
disp(['Performing steps: ' num2str(roundTypes)]); disp(['Performing steps: ' num2str(roundTypes)]);
end end
for n = 1:length(roundTypes) for n = 1:length(roundTypes)
round = roundTypes(n); round = roundTypes(n);
@ -465,7 +465,7 @@ for run = 1:nruns
npops = poistaTyhjatPopulaatiot(npops); npops = poistaTyhjatPopulaatiot(npops);
POP_LOGML = computePopulationLogml(1:npops, adjprior, priorTerm); POP_LOGML = computePopulationLogml(1:npops, adjprior, priorTerm);
if dispText if dispText
disp(['Found partition with ' num2str(npops) ' populations.']); disp(['Found partition with ' num2str(npops) ' populations.']);
disp(['Log(ml) = ' num2str(logml)]); disp(['Log(ml) = ' num2str(logml)]);
disp(' '); disp(' ');
@ -491,7 +491,7 @@ COUNTS = countsBest;
SUMCOUNTS = sumCountsBest; SUMCOUNTS = sumCountsBest;
POP_LOGML = pop_logmlBest; POP_LOGML = pop_logmlBest;
LOGDIFF = logdiffbest; LOGDIFF = logdiffbest;
%-------------------------------------------------------------------------- %--------------------------------------------------------------------------
function clearGlobalVars function clearGlobalVars
@ -509,9 +509,9 @@ function Z = linkage(Y, method)
[k, n] = size(Y); [k, n] = size(Y);
m = (1+sqrt(1+8*n))/2; m = (1+sqrt(1+8*n))/2;
if k ~= 1 | m ~= fix(m) if k ~= 1 | m ~= fix(m)
error('The first input has to match the output of the PDIST function in size.'); error('The first input has to match the output of the PDIST function in size.');
end end
if nargin == 1 % set default switch to be 'co' if nargin == 1 % set default switch to be 'co'
method = 'co'; method = 'co';
end end
method = lower(method(1:2)); % simplify the switch string. method = lower(method(1:2)); % simplify the switch string.
@ -519,19 +519,19 @@ monotonic = 1;
Z = zeros(m-1,3); % allocate the output matrix. Z = zeros(m-1,3); % allocate the output matrix.
N = zeros(1,2*m-1); N = zeros(1,2*m-1);
N(1:m) = 1; N(1:m) = 1;
n = m; % since m is changing, we need to save m in n. n = m; % since m is changing, we need to save m in n.
R = 1:n; R = 1:n;
for s = 1:(n-1) for s = 1:(n-1)
X = Y; X = Y;
[v, k] = min(X); [v, k] = min(X);
i = floor(m+1/2-sqrt(m^2-m+1/4-2*(k-1))); i = floor(m+1/2-sqrt(m^2-m+1/4-2*(k-1)));
j = k - (i-1)*(m-i/2)+i; j = k - (i-1)*(m-i/2)+i;
Z(s,:) = [R(i) R(j) v]; % update one more row to the output matrix A Z(s,:) = [R(i) R(j) v]; % update one more row to the output matrix A
I1 = 1:(i-1); I2 = (i+1):(j-1); I3 = (j+1):m; % these are temp variables. I1 = 1:(i-1); I2 = (i+1):(j-1); I3 = (j+1):m; % these are temp variables.
U = [I1 I2 I3]; U = [I1 I2 I3];
I = [I1.*(m-(I1+1)/2)-m+i i*(m-(i+1)/2)-m+I2 i*(m-(i+1)/2)-m+I3]; I = [I1.*(m-(I1+1)/2)-m+i i*(m-(i+1)/2)-m+I2 i*(m-(i+1)/2)-m+I3];
J = [I1.*(m-(I1+1)/2)-m+j I2.*(m-(I2+1)/2)-m+j j*(m-(j+1)/2)-m+I3]; J = [I1.*(m-(I1+1)/2)-m+j I2.*(m-(I2+1)/2)-m+j j*(m-(j+1)/2)-m+I3];
switch method switch method
case 'si' %single linkage case 'si' %single linkage
Y(I) = min(Y(I),Y(J)); Y(I) = min(Y(I),Y(J));
@ -548,12 +548,12 @@ for s = 1:(n-1)
end end
J = [J i*(m-(i+1)/2)-m+j]; J = [J i*(m-(i+1)/2)-m+j];
Y(J) = []; % no need for the cluster information about j. Y(J) = []; % no need for the cluster information about j.
% update m, N, R % update m, N, R
m = m-1; m = m-1;
N(n+s) = N(R(i)) + N(R(j)); N(n+s) = N(R(i)) + N(R(j));
R(i) = n+s; R(i) = n+s;
R(j:(n-1))=R((j+1):n); R(j:(n-1))=R((j+1):n);
end end
@ -623,7 +623,7 @@ function [muutokset, diffInCounts] = ...
% %
% Lisäys 25.9.2007: % Lisäys 25.9.2007:
% Otettu käyttöön globaali muuttuja LOGDIFF, johon on tallennettu muutokset % Otettu käyttöön globaali muuttuja LOGDIFF, johon on tallennettu muutokset
% logml:ssä siirrettäessä yksilöitä toisiin populaatioihin. % logml:ssä siirrettäessä yksilöitä toisiin populaatioihin.
global COUNTS; global SUMCOUNTS; global COUNTS; global SUMCOUNTS;
global PARTITION; global POP_LOGML; global PARTITION; global POP_LOGML;
@ -647,7 +647,7 @@ COUNTS(:,:,i1) = COUNTS(:,:,i1)+diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)+diffInSumCounts; SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)+diffInSumCounts;
i2 = find(muutokset==-Inf); % Etsitään populaatiot jotka muuttuneet viime kerran jälkeen. i2 = find(muutokset==-Inf); % Etsitään populaatiot jotka muuttuneet viime kerran jälkeen.
i2 = setdiff(i2,i1); i2 = setdiff(i2,i1);
i2_logml = POP_LOGML(i2); i2_logml = POP_LOGML(i2);
ni2 = length(i2); ni2 = length(i2);
@ -668,19 +668,19 @@ LOGDIFF(ind,:) = muutokset;
function diffInCounts = computeDiffInCounts(rows, max_noalle, nloci, data) function diffInCounts = computeDiffInCounts(rows, max_noalle, nloci, data)
% Muodostaa max_noalle*nloci taulukon, jossa on niiden alleelien % Muodostaa max_noalle*nloci taulukon, jossa on niiden alleelien
% lukumäärät (vastaavasti kuin COUNTS:issa), jotka ovat data:n % lukumäärät (vastaavasti kuin COUNTS:issa), jotka ovat data:n
% riveillä rows. rows pitää olla vaakavektori. % riveillä rows. rows pitää olla vaakavektori.
diffInCounts = zeros(max_noalle, nloci); diffInCounts = zeros(max_noalle, nloci);
for i=rows for i=rows
row = data(i,:); row = data(i,:);
notEmpty = find(row>=0); notEmpty = find(row>=0);
if length(notEmpty)>0 if length(notEmpty)>0
diffInCounts(row(notEmpty) + (notEmpty-1)*max_noalle) = ... diffInCounts(row(notEmpty) + (notEmpty-1)*max_noalle) = ...
diffInCounts(row(notEmpty) + (notEmpty-1)*max_noalle) + 1; diffInCounts(row(notEmpty) + (notEmpty-1)*max_noalle) + 1;
end end
end end
%------------------------------------------------------------------------ %------------------------------------------------------------------------
@ -693,8 +693,8 @@ function updateGlobalVariables(ind, i2, diffInCounts, ...
% Suorittaa globaalien muuttujien muutokset, kun yksilö ind % Suorittaa globaalien muuttujien muutokset, kun yksilö ind
% on siirretään koriin i2. % on siirretään koriin i2.
global PARTITION; global PARTITION;
global COUNTS; global COUNTS;
global SUMCOUNTS; global SUMCOUNTS;
global POP_LOGML; global POP_LOGML;
global LOGDIFF; global LOGDIFF;
@ -724,7 +724,7 @@ function [muutokset, diffInCounts] = laskeMuutokset2( ...
i1, globalRows, data, adjprior, priorTerm); i1, globalRows, data, adjprior, priorTerm);
% Palauttaa npops*1 taulun, jossa i:s alkio kertoo, mikä olisi % Palauttaa npops*1 taulun, jossa i:s alkio kertoo, mikä olisi
% muutos logml:ssä, mikäli korin i1 kaikki yksilöt siirretään % muutos logml:ssä, mikäli korin i1 kaikki yksilöt siirretään
% koriin i. % koriin i.
global COUNTS; global SUMCOUNTS; global COUNTS; global SUMCOUNTS;
global PARTITION; global POP_LOGML; global PARTITION; global POP_LOGML;
@ -839,7 +839,7 @@ for pop2 = 1:npops2
i2 = [1:i1-1 , i1+1:npops]; i2 = [1:i1-1 , i1+1:npops];
i2_logml = POP_LOGML(i2)'; i2_logml = POP_LOGML(i2)';
COUNTS(:,:,i2) = COUNTS(:,:,i2)+repmat(diffInCounts, [1 1 npops-1]); COUNTS(:,:,i2) = COUNTS(:,:,i2)+repmat(diffInCounts, [1 1 npops-1]);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)+repmat(diffInSumCounts,[npops-1 1]); SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)+repmat(diffInSumCounts,[npops-1 1]);
new_i2_logml = computePopulationLogml(i2, adjprior, priorTerm)'; new_i2_logml = computePopulationLogml(i2, adjprior, priorTerm)';
@ -848,7 +848,7 @@ for pop2 = 1:npops2
muutokset(pop2,i2) = new_i1_logml - i1_logml ... muutokset(pop2,i2) = new_i1_logml - i1_logml ...
+ new_i2_logml - i2_logml; + new_i2_logml - i2_logml;
end end
end end
%------------------------------------------------------------------------------------ %------------------------------------------------------------------------------------
@ -858,7 +858,7 @@ function muutokset = laskeMuutokset5(inds, globalRows, data, adjprior, ...
% Palauttaa length(inds)*1 taulun, jossa i:s alkio kertoo, mikä olisi % Palauttaa length(inds)*1 taulun, jossa i:s alkio kertoo, mikä olisi
% muutos logml:ssä, mikäli yksilö i vaihtaisi koria i1:n ja i2:n välillä. % muutos logml:ssä, mikäli yksilö i vaihtaisi koria i1:n ja i2:n välillä.
global COUNTS; global SUMCOUNTS; global COUNTS; global SUMCOUNTS;
global PARTITION; global POP_LOGML; global PARTITION; global POP_LOGML;
@ -885,14 +885,14 @@ for i = 1:ninds
SUMCOUNTS(pop1,:) = SUMCOUNTS(pop1,:)-diffInSumCounts; SUMCOUNTS(pop1,:) = SUMCOUNTS(pop1,:)-diffInSumCounts;
COUNTS(:,:,pop2) = COUNTS(:,:,pop2)+diffInCounts; COUNTS(:,:,pop2) = COUNTS(:,:,pop2)+diffInCounts;
SUMCOUNTS(pop2,:) = SUMCOUNTS(pop2,:)+diffInSumCounts; SUMCOUNTS(pop2,:) = SUMCOUNTS(pop2,:)+diffInSumCounts;
new_logmls = computePopulationLogml([i1 i2], adjprior, priorTerm); new_logmls = computePopulationLogml([i1 i2], adjprior, priorTerm);
muutokset(i) = sum(new_logmls); muutokset(i) = sum(new_logmls);
COUNTS(:,:,pop1) = COUNTS(:,:,pop1)+diffInCounts; COUNTS(:,:,pop1) = COUNTS(:,:,pop1)+diffInCounts;
SUMCOUNTS(pop1,:) = SUMCOUNTS(pop1,:)+diffInSumCounts; SUMCOUNTS(pop1,:) = SUMCOUNTS(pop1,:)+diffInSumCounts;
COUNTS(:,:,pop2) = COUNTS(:,:,pop2)-diffInCounts; COUNTS(:,:,pop2) = COUNTS(:,:,pop2)-diffInCounts;
SUMCOUNTS(pop2,:) = SUMCOUNTS(pop2,:)-diffInSumCounts; SUMCOUNTS(pop2,:) = SUMCOUNTS(pop2,:)-diffInSumCounts;
end end
muutokset = muutokset - i1_logml - i2_logml; muutokset = muutokset - i1_logml - i2_logml;
@ -952,7 +952,7 @@ dist2 = dist(apu);
function npops = poistaTyhjatPopulaatiot(npops) function npops = poistaTyhjatPopulaatiot(npops)
% Poistaa tyhjentyneet populaatiot COUNTS:ista ja % Poistaa tyhjentyneet populaatiot COUNTS:ista ja
% SUMCOUNTS:ista. Päivittää npops:in ja PARTITION:in. % SUMCOUNTS:ista. Päivittää npops:in ja PARTITION:in.
global COUNTS; global COUNTS;
@ -1006,7 +1006,7 @@ if abs(logml)<10000
end end
if logml<0 if logml<0
mjono(pointer-1) = '-'; mjono(pointer-1) = '-';
end end
else else
suurinYks = 4; suurinYks = 4;
while abs(logml)/(10^(suurinYks+1)) >= 1 while abs(logml)/(10^(suurinYks+1)) >= 1
@ -1035,8 +1035,8 @@ end
function digit = palautaYks(num,yks) function digit = palautaYks(num,yks)
% palauttaa luvun num 10^yks termin kertoimen % palauttaa luvun num 10^yks termin kertoimen
% string:inä % string:inä
% yks täytyy olla kokonaisluku, joka on % yks täytyy olla kokonaisluku, joka on
% vähintään -1:n suuruinen. Pienemmillä % vähintään -1:n suuruinen. Pienemmillä
% luvuilla tapahtuu jokin pyöristysvirhe. % luvuilla tapahtuu jokin pyöristysvirhe.
@ -1063,7 +1063,7 @@ if abs(div)<100
if arvo>0 if arvo>0
mjono(1) = num2str(arvo); mjono(1) = num2str(arvo);
end end
else else
suurinYks = floor(log10(div)); suurinYks = floor(log10(div));
mjono(6) = num2str(suurinYks); mjono(6) = num2str(suurinYks);
@ -1125,7 +1125,7 @@ T = zeros(m,1);
end end
end end
end end
function T = clusternum(X, T, k, c) function T = clusternum(X, T, k, c)
m = size(X,1)+1; m = size(X,1)+1;
while(~isempty(k)) while(~isempty(k))
@ -1136,7 +1136,7 @@ while(~isempty(k))
% Assign this node number to leaf children % Assign this node number to leaf children
t = (children<=m); t = (children<=m);
T(children(t)) = c; T(children(t)) = c;
% Move to next level % Move to next level
k = children(~t) - m; k = children(~t) - m;
end end

28
tests/testthat/test-greedyMix.R
View file

@ -1,11 +1,11 @@
context("Auxiliary functions to greedyMix") context("Auxiliary functions to greedyMix")
# Defining the relative path to current inst ----------------------------------- # Defining the relative path to current inst -----------------------------------
path_inst <- system.file("ext", "", package = "rBAPS") path_inst <- system.file("extdata", "", package = "rBAPS")
# Reading datasets ------------------------------------------------------------- # Reading datasets -------------------------------------------------------------
baps_diploid <- read.delim( baps_diploid <- read.delim(
file = paste(path_inst, "BAPS_format_clustering_diploid.txt", sep = "/"), file = file.path(path_inst, "BAPS_format_clustering_diploid.txt"),
sep = " ", sep = " ",
header = FALSE header = FALSE
) )
@ -31,26 +31,27 @@ test_that("handleData works as expected", {
expect_equal(data_obs, data_exp) expect_equal(data_obs, data_exp)
}) })
context("Opening files on greedyMix") context("Processing files through greedyMix")
df_fasta <- greedyMix( raw_fasta <- importFile(
data = file.path(path_inst, "FASTA_clustering_haploid.fasta"), data = file.path(path_inst, "FASTA_clustering_haploid.fasta"),
format = "FASTA" format = "FASTA"
) )
df_vcf <- greedyMix( raw_vcf <- importFile(
data = file.path(path_inst, "vcf_example.vcf"), data = file.path(path_inst, "vcf_example.vcf"),
format = "VCF", format = "VCF",
verbose = FALSE verbose = FALSE
) )
df_bam <- greedyMix( df_bam <- importFile(
data = file.path(path_inst, "bam_example.bam"), data = file.path(path_inst, "bam_example.bam"),
format = "BAM", format = "BAM",
) )
test_that("Files are imported correctly", { test_that("Files are imported correctly", {
expect_equal(dim(df_fasta), c(5, 99)) expect_equal(dim(raw_fasta), c(5, 99))
expect_equal(dim(df_vcf), c(variants = 2, fix_cols = 8, gt_cols = 3)) expect_equal(dim(raw_vcf), c(variants = 2, fix_cols = 8, gt_cols = 3))
expect_error( expect_error(
greedyMix( importFile(
data = paste(path_inst, "sam_example.sam", sep = "/"), data = paste(path_inst, "sam_example.sam", sep = "/"),
format = "SAM", format = "SAM",
) )
@ -58,6 +59,15 @@ test_that("Files are imported correctly", {
expect_equal(length(df_bam[[1]]), 13) expect_equal(length(df_bam[[1]]), 13)
}) })
df_fasta <- greedyMix(
data = file.path(path_inst, "FASTA_clustering_haploid.fasta"),
format = "FASTA"
)
test_that("greedyMix() works", {
expect_error(greedyMix(file.path(path_inst, "vcf_example.vcf")))
expect_error(greedyMix(file.path(path_inst, "bam_example.bam")))
})
context("Linkage") context("Linkage")
test_that("Linkages are properly calculated", { test_that("Linkages are properly calculated", {

2
tests/testthat/test-spatialMixture.R
View file

@ -79,7 +79,7 @@ test_that("lakseKlitik() and subfunctions produce expected output", {
}) })
test_that("testFastaData() produces same output as on MATLAB", { test_that("testFastaData() produces same output as on MATLAB", {
msa <- system.file("ext", "seqs.fa", package = "rBAPS") msa <- system.file("extdata", "seqs.fa", package = "rBAPS")
test_msa <- testFastaData(msa) test_msa <- testFastaData(msa)
expect_equal(test_msa$ninds, 515) expect_equal(test_msa$ninds, 515)
expect_equal(dim(test_msa$data), c(515, 745)) expect_equal(dim(test_msa$data), c(515, 745))