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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
CITATION.cff
.travis.yml
inst/ext/ExamplesDataFormatting
inst/testdata
.github
aux

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

39
NAMESPACE
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@ -1,45 +1,8 @@
# 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(greedyPopMix)
export(handleData)
export(handlePopData)
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)
export(importFile)
importFrom(R6,R6Class)
importFrom(Rsamtools,scanBam)
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 divider divider
#' @return data (after alleles were added)
#' @export
addAlleles <- function(data, ind, line, divider) {
# Lisaa BAPS-formaatissa olevaan datataulukkoon
# 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
#' @param tietue tietue
#' @importFrom methods is
#' @export
admix1 <- function(tietue) {
if (!is.list(tietue)) {
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.
#' @param points points
#' @param fii fii
#' @export
calculatePopLogml <- function(points, fii) {
n <- length(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
#' j 1/noalle(j) verran.
#' @param noalle noalle
#' @export
computeAllFreqs2 <- function(noalle) {
COUNTS <- ifelse(isGlobalEmpty(COUNTS), vector(), COUNTS)
SUMCOUNTS <- ifelse(isGlobalEmpty(SUMCOUNTS), vector(), COUNTS)

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

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

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

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

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

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

96
R/greedyMix.R
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@ -1,6 +1,16 @@
#' @title Clustering of individuals
#' @param data data file
#' @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
#' @importFrom utils read.delim
#' @importFrom vcfR read.vcfR
@ -9,41 +19,59 @@
#' @references Samtools: a suite of programs for interacting
#' with high-throughput sequencing data. <http://www.htslib.org/>
#' @export
greedyMix <- function(data, format, verbose = TRUE) {
# Parsing data format ------------------------------------------------------
#' @examples
#' 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)) {
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'"
# Comparing partitions =======================================================
if (!is.null(partitionCompare)) {
logmls <- comparePartitions(
c[["data"]], nrow(c[["data"]]), partitionCompare[["partitions"]], ninds,
c[["rowsFromInd"]], c[["noalle"]], c[["adjprior"]]
)
} 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
#' @references Samtools: a suite of programs for interacting
#' with high-throughput sequencing data. <http://www.htslib.org/>
#' @export
greedyPopMix <- function(data, format, partitionCompare = NULL, verbose = TRUE
) {
# Replacing original file reading code with greedyMix()

36
R/handleData.R
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@ -1,5 +1,6 @@
#' @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
#' 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.
@ -7,9 +8,9 @@
#' 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,
#' 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
handleData <- function(raw_data) {
handleData <- function(raw_data, format = "Genepop") {
# Alkuper?isen datan viimeinen sarake kertoo, milt?yksil?lt?
# kyseinen rivi on per?isin. Funktio tutkii ensin, ett?montako
# 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.
# T?m?n j?lkeen funktio muuttaa alleelikoodit siten, ett?yhden lokuksen 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)
nloci <- size(raw_data, 2) - 1
dataApu <- data[, 1:nloci]
nollat <- matlab2r::find(dataApu == 0)
if (!isempty(nollat)) {
isoinAlleeli <- base::max(max(dataApu))
isoinAlleeli <- base::max(base::max(dataApu))
dataApu[nollat] <- isoinAlleeli + 1
data[, 1:nloci] <- dataApu
}
# dataApu <- []
# nollat <- []
# isoinAlleeli <- []
noalle <- zeros(1, nloci)
alleelitLokuksessa <- cell(nloci, 1, expandable = TRUE)
for (i in 1:nloci) {
alleelitLokuksessaI <- unique(data[, i])
alleelitLokuksessa[[i]] <- sort(alleelitLokuksessaI[
matlab2r::find(
alleelitLokuksessaI >= 0
)
matlab2r::find(alleelitLokuksessaI >= 0)
])
noalle[i] <- length(alleelitLokuksessa[[i]])
}
@ -49,9 +51,7 @@ handleData <- function(raw_data) {
for (i in 1:nloci) {
alleelitLokuksessaI <- alleelitLokuksessa[[i]]
puuttuvia <- base::max(noalle) - length(alleelitLokuksessaI)
alleleCodes[, i] <- as.matrix(
c(alleelitLokuksessaI, zeros(puuttuvia, 1))
)
alleleCodes[, i] <- as.matrix(c(alleelitLokuksessaI, zeros(puuttuvia, 1)))
}
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)
ncols <- size(data, 2)
rowsFromInd <- zeros(nind, 1)
@ -71,11 +71,11 @@ handleData <- function(raw_data) {
a <- -999
emptyRow <- repmat(a, c(1, ncols))
lessThanMax <- matlab2r::find(rowsFromInd < maxRowsFromInd)
missingRows <- maxRowsFromInd * nind - nrows
missingRows <- max(maxRowsFromInd * nind - nrows, 0L)
data <- rbind(data, zeros(missingRows, ncols))
pointer <- 1
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
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].
#' Before this change, an allele whose code is zero is changed.
#' @param raw_data raw data
#' @export
handlePopData <- function(raw_data) {
# Alkuperäisen datan viimeinen sarake kertoo, milt?yksilölt?
# 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
# clustering.
# 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")) {
dist <- c$dist
Z <- c$Z
} else {
Z <- NULL
}
rm(c)
nargin <- length(as.list(match.call())) - 1
if (nargin < 2) {
@ -65,14 +68,14 @@ indMix <- function(c, npops, dispText = TRUE) {
nruns <- length(npopsTaulu)
initData <- data
data <- data[, 1:(ncol(data) - 1)]
data <- data[, seq_along(noalle)] # Original code always dropped last column.
logmlBest <- -1e50
partitionSummary <- -1e50 * ones(30, 2) # Tiedot 30 parhaasta partitiosta (npops ja logml)
partitionSummary[, 1] <- zeros(30, 1)
worstLogml <- -1e50
worstIndex <- 1
for (run in 1:nruns) {
for (run in seq_along(nruns)) {
npops <- npopsTaulu[[run]]
if (dispText) {
dispLine()
@ -84,6 +87,7 @@ indMix <- function(c, npops, dispText = TRUE) {
)
}
ninds <- size(rows, 1)
initialPartition <- admixture_initialization(initData, npops, Z)
sumcounts_counts_logml <- initialCounts(
initialPartition, data, npops, rows, noalle, adjprior
@ -93,16 +97,15 @@ indMix <- function(c, npops, dispText = TRUE) {
logml <- sumcounts_counts_logml$logml
PARTITION <- zeros(ninds, 1)
for (i in 1:ninds) {
for (i in seq_len(ninds)) {
apu <- rows[i]
PARTITION[i] <- initialPartition[apu[1]]
}
COUNTS <- counts
SUMCOUNTS <- sumcounts
POP_LOGML <- computePopulationLogml(1:npops, adjprior, priorTerm)
POP_LOGML <- computePopulationLogml(seq_len(npops), adjprior, priorTerm)
LOGDIFF <- repmat(-Inf, c(ninds, npops))
rm(initialPartition, counts, sumcounts)
# PARHAAN MIXTURE-PARTITION ETSIMINEN
nRoundTypes <- 7
@ -120,30 +123,34 @@ indMix <- function(c, npops, dispText = TRUE) {
)
}
iter <- 1L
while (ready != 1) {
# FIXME: loop caught in here
iter <- iter + 1L
if (iter > max_iter) {
warning("max_iter reached. Stopping prematurely.")
break
}
muutoksia <- 0
if (dispText) {
message(paste("\nPerforming steps:", as.character(roundTypes)))
message("Performing steps: ", paste(roundTypes, collapse = " "))
}
for (n in 1:length(roundTypes)) {
for (n in seq_along(roundTypes)) {
round <- roundTypes[n]
kivaluku <- 0
if (kokeiltu[round] == 1) { # Askelta kokeiltu viime muutoksen j<>lkeen
} else if (round == 0 | round == 1) { # Yksil<69>n siirt<72>minen toiseen populaatioon.
inds <- 1:ninds
aputaulu <- cbind(inds, rand(ninds, 1))
aputaulu <- sortrows(aputaulu, 2)
inds <- seq_len(ninds)
aputaulu <- cbind(t(inds), rand(ninds, 1))
aputaulu <- matrix(sortrows(aputaulu, 2), nrow = nrow(aputaulu))
inds <- t(aputaulu[, 1])
muutosNyt <- 0
for (ind in inds) {
i1 <- PARTITION[ind]
muutokset_diffInCounts <- greedyMix_muutokset$new()
# FIXME: using 100-length global variables instead of the ones in this function
muutokset_diffInCounts <- muutokset_diffInCounts$laskeMuutokset(
ind, rows, data, adjprior, priorTerm
)
@ -190,7 +197,7 @@ indMix <- function(c, npops, dispText = TRUE) {
}
} else if (round == 2) { # Populaation yhdist<73>minen toiseen.
maxMuutos <- 0
for (pop in 1:npops) {
for (pop in seq_len(npops)) {
muutokset_diffInCounts <- greedyMix_muutokset$new()
muutokset_diffInCounts <- muutokset_diffInCounts$laskeMuutokset2(
pop, rows, data, adjprior, priorTerm
@ -234,7 +241,7 @@ indMix <- function(c, npops, dispText = TRUE) {
} else if (round == 3 || round == 4) { # Populaation jakaminen osiin.
maxMuutos <- 0
ninds <- size(rows, 1)
for (pop in 1:npops) {
for (pop in seq_len(npops)) {
inds2 <- matlab2r::find(PARTITION == pop)
ninds2 <- length(inds2)
if (ninds2 > 2) {
@ -265,7 +272,7 @@ indMix <- function(c, npops, dispText = TRUE) {
muutoksia <- 1
kokeiltu <- zeros(nRoundTypes, 1)
rivit <- list()
for (i in 1:length(muuttuvat)) {
for (i in seq_len(muuttuvat)) {
ind <- muuttuvat[i]
lisa <- rows[ind, 1]:rows[ind, 2]
rivit <- rbind(rivit, t(lisa))
@ -421,7 +428,7 @@ indMix <- function(c, npops, dispText = TRUE) {
totalMuutos <- muutokset(1, emptyPop)
rivit <- list()
for (i in 1:length(muuttuvat)) {
for (i in seq_len(muuttuvat)) {
ind <- muuttuvat[i]
lisa <- rows[ind, 1]:rows[ind, 2]
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 (vaihe <= 4) {
vaihe <= vaihe + 1
@ -536,11 +541,10 @@ indMix <- function(c, npops, dispText = TRUE) {
# TALLENNETAAN
npops <- poistaTyhjatPopulaatiot(npops)
POP_LOGML <- computePopulationLogml(1:npops, adjprior, priorTerm)
POP_LOGML <- computePopulationLogml(seq_len(npops), adjprior, priorTerm)
if (dispText) {
print(paste("Found partition with", as.character(npops), "populations."))
print(paste("Log(ml) =", as.character(logml)))
print(" ")
message("Found partition with ", as.character(npops), " populations.")
message("Log(ml) = ", as.character(logml))
}
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
#' @param nameFile nameFile
#' @param indexFile indexFile
#' @export
initPopNames <- function(nameFile, indexFile) {
# Palauttaa tyhj<68>n, mik<69>li nimitiedosto ja indeksitiedosto
# 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)
ninds <- size(rows, 1)
koot <- rows[, 1] - rows[, 2] + 1
koot <- rows[1] - rows[2] + 1
maxSize <- base::max(koot)
counts <- zeros(base::max(noalle), nloci, npops)

4
R/laskeLoggis.R
View file

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

7
R/laskeMuutokset12345.R
View file

@ -349,12 +349,15 @@ greedyMix_muutokset <- R6Class(
i1_logml <- POP_LOGML[i1]
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(
rows, size(COUNTS, 1), size(COUNTS, 2), data
)
diffInSumCounts <- colSums(diffInCounts)
COUNTS[, , i1] <- COUNTS[, , i1] - diffInCounts
SUMCOUNTS[i1, ] <- SUMCOUNTS[i1, ] - diffInSumCounts
new_i1_logml <- computePopulationLogml(i1, adjprior, priorTerm)

1
R/learn_partition_modified.R
View file

@ -1,5 +1,4 @@
#' @title Learn partition (modified)
#' @export
#' @param ordered ordered
#' @return part
#' @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
#' @description Goes through all the ways to divide the points into two or
#' three groups. Chooses the partition which obtains highest logml.
#' @export
learn_simple_partition <- function(ordered_points, fii) {
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
#' this function is part of this package (instead of a MATLAB-replicating
#' package such as matlab2r)
#' @export
linkage <- function(Y, method = "co") {
k <- size(Y)[1]
n <- size(Y)[2]

25
R/load_fasta.R
View file

@ -4,18 +4,19 @@
#' 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 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
#'
#' @examples
#' msa <- system.file("ext", "seqs.fa", package = "rBAPS")
#' snp.matrix <- load_fasta(msa)
#' msa <- system.file("extdata", "seqs.fa", package = "rBAPS")
#' snp.matrix <- rBAPS:::load_fasta(msa)
#' @author Gerry Tonkin-Hill, Waldir Leoncio
#' @seealso rhierbaps::load_fasta
#' @importFrom ape read.FASTA as.DNAbin
#' @export
load_fasta <- function(msa, keep.singletons = FALSE) {
load_fasta <- function(msa, keep_singletons = FALSE, output_numbers = TRUE) {
# Check inputs
if (is(msa, "character")) {
@ -28,7 +29,9 @@ load_fasta <- function(msa, keep.singletons = FALSE) {
} else {
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.
seq_names <- labels(seqs)
@ -46,8 +49,8 @@ load_fasta <- function(msa, keep.singletons = FALSE) {
conserved <- colSums(t(t(seqs) == seqs[1, ])) == nrow(seqs)
seqs <- seqs[, !conserved]
if (!keep.singletons) {
# remove singletons as they are uninformative in the algorithm
if (!keep_singletons) {
# remove_singletons as they are uninformative in the algorithm
is_singleton <- apply(seqs, 2, function(x) {
tab <- table(x)
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
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)
}

1
R/logml2String.R
View file

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

1
R/lueGenePopData.R
View file

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

1
R/lueGenePopDataPop.R
View file

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

1
R/noIndex.R
View file

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

1
R/ownNum2Str.R
View file

@ -2,7 +2,6 @@
#' @description Converts numbers to strings
#' @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.
#' @export
ownNum2Str <- function(number) {
absolute <- abs(number)
if (absolute < 1000) {

1
R/poistaLiianPienet.R
View file

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

4
R/poistaTyhjatPopulaatiot.R
View file

@ -1,13 +1,13 @@
poistaTyhjatPopulaatiot <- function(npops) {
# % Poistaa tyhjentyneet populaatiot COUNTS:ista ja
# % 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]
SUMCOUNTS <- SUMCOUNTS[notEmpty, ]
LOGDIFF <- LOGDIFF[, notEmpty]
for (n in 1:length(notEmpty)) {
apu <- matlab2r::find(PARTITION == notEmpty(n))
apu <- matlab2r::find(PARTITION == notEmpty[n])
PARTITION[apu] <- n
}
npops <- length(notEmpty)

1
R/proportion2str.R
View file

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

3
R/randdir.R
View file

@ -1,10 +1,9 @@
#' @title Generates random numbers
#' @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 nc number of rows on output
#' @seealso randga
#' @export
randdir <- function(counts, nc) {
svar <- zeros(nc, 1)
for (i in 1:nc) {

1
R/rivinSisaltamienMjonojenLkm.R
View file

@ -2,7 +2,6 @@
#' @param line line number
#' @return count
#' @description Returns the number of queues contained in the line. There must be a space between the queues.
#' @export
rivinSisaltamienMjonojenLkm <- function(line) {
# 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.

1
R/selvitaDigitFormat.R
View file

@ -1,7 +1,6 @@
#' @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.
#' @return df
#' @export
selvitaDigitFormat <- function(line) {
# line on ensimm<6D>inen pop-sanan j<>lkeinen rivi
# Genepop-formaatissa olevasta datasta. funktio selvitt<74><74>

2
R/simulateAllFreqs.R
View file

@ -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.
#' 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
#' @export
simulateAllFreqs <- function(noalle) {
if (isGlobalEmpty(COUNTS)) {
max_noalle <- 0

2
R/simulateIndividuals.R
View file

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

2
R/simuloiAlleeli.R
View file

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

1
R/suoritaMuutos.R
View file

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

1
R/takeLine.R
View file

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

1
R/testaaKoordinaatit.R
View file

@ -3,7 +3,6 @@
#' @param coordinates coordinates
#' @param interactive prompt user for relevant questions during execution
#' @return a list of defectives ("viallinen") and coordinates
#' @export
testaaKoordinaatit <- function(ninds, coordinates, interactive = TRUE) {
# Testaa onko koordinaatit kunnollisia.
# 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
#' @param data dataset
#' @return ninds
#' @export
testaaOnkoKunnollinenBapsData <- function(data) {
# Tarkastaa onko viimeisess?sarakkeessa kaikki
# luvut 1,2,...,n johonkin n:<3A><>n asti.

1
R/testaaPop.R
View file

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

77
R/writeMixtureInfo.R
View file

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

1
R/writeMixtureInfoPop.R
View file

@ -10,7 +10,6 @@
#' @param partitionSummary partitionSummary
#' @param popnames popnames
#' @param fixedK fixedK
#' @export
writeMixtureInfoPop <- function(logml, rows, data, adjprior, priorTerm,
outPutFile, inputFile, partitionSummary,
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 54 82 102 92 116 90 86 104 1
88 104 56 84 102 -9 120 90 88 100 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 56 80 100 90 118 90 -9 104 3
80 102 54 82 102 92 116 90 86 104 4
88 104 56 84 102 92 120 90 88 100 4
86 102 56 80 -9 90 116 90 86 100 5
88 102 54 80 -9 90 116 92 86 100 5
-9 102 56 80 100 90 118 90 88 104 1
-9 102 54 82 102 92 116 90 86 104 1
88 104 56 84 102 -9 120 90 88 100 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 56 80 100 90 118 90 -9 104 3
80 102 54 82 102 92 116 90 86 104 4
88 104 56 84 102 92 120 90 88 100 4
86 102 56 80 -9 90 116 90 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
AACGAAACGATCGCGTCACCGGAACGTTGTCCGTCTCGAATAGCACTGTGGGAACGTGTTTTACATTCGT
TAGTAACATGGTCAGCTGCTCATCCGTATT
>2
ATCAGCAAACGAGAAGTTGCAGAGGTCTTTGGTTTGAGCATTGCCCCCATACAATCGACTTCTGGCCTGG
AATGCACCACAAACATACCCCACAGGCTCG
>3
GCTTTTACTAAGGCCTATCGGATTCAACGTCACTAAGACTCGGCACTAACAGGCCGTTGTAAGCCGCTCT
GTCTGAGTATGGATGGTGGAGGCGGAGCCG
>4
ACCTGGACCTCTGTATTAACGGCTGTGATTCTGAGGGGGGTATCGCAGCGCACTTTCTAGCTATATCACG
CAAGGATAAAGTTCACCCATCACGTTGACC
>5
ACAATACGTCATCCACACCGCGCCTATGGAAGAATTTGCCCTTTCGGCGACAGCCCATGCTGTCAAGGAG
GTAACATAGCTACCAGGTCCCATTCCAGGA
>1
AACGAAACGATCGCGTCACCGGAACGTTGTCCGTCTCGAATAGCACTGTGGGAACGTGTTTTACATTCGT
TAGTAACATGGTCAGCTGCTCATCCGTATT
>2
ATCAGCAAACGAGAAGTTGCAGAGGTCTTTGGTTTGAGCATTGCCCCCATACAATCGACTTCTGGCCTGG
AATGCACCACAAACATACCCCACAGGCTCG
>3
GCTTTTACTAAGGCCTATCGGATTCAACGTCACTAAGACTCGGCACTAACAGGCCGTTGTAAGCCGCTCT
GTCTGAGTATGGATGGTGGAGGCGGAGCCG
>4
ACCTGGACCTCTGTATTAACGGCTGTGATTCTGAGGGGGGTATCGCAGCGCACTTTCTAGCTATATCACG
CAAGGATAAAGTTCACCCATCACGTTGACC
>5
ACAATACGTCATCCACACCGCGCCTATGGAAGAATTTGCCCTTTCGGCGACAGCCCATGCTGTCAAGGAG
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
1
2
3
4
5
6
7
8
9
10
POP
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_3, 0000 1010 1414 0404 0505 0606 1111 1212 0707 1212
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_6, 1111 1010 1414 0404 0505 0606 1111 1212 0707 1010
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_9, 0909 1010 1010 0404 0505 0606 1919 1212 0707 1010
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_12, 1515 1010 0606 0404 0505 0606 1616 1212 0202 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_15, 1111 1010 1414 0404 0505 0606 1616 0606 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_18, 1515 1010 1212 0404 0505 0606 0909 1212 0707 1010
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_21, 1515 1212 1212 0404 0505 0606 0808 1818 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_24, 0000 0000 0000 0404 0505 0606 1919 1212 0404 0000
Area1_25, 0000 0000 0000 0404 0202 0101 1919 1919 0707 1212
Area1_26, 1313 1010 0202 0404 0505 0606 1717 1212 0707 1212
POP
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_3, 1711 0000 1412 0402 0000 0000 1913 1002 0000 1313
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_6, 1309 0000 1212 0404 0603 0802 1311 1204 0903 1309
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_9, 0000 1212 1209 0404 0505 0606 0000 1106 0707 1208
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_12, 1807 0000 1212 0505 0606 0000 0909 0401 0909 1309
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_15, 1817 0707 1212 0505 0707 0909 1111 1004 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_18, 0707 0606 1408 0404 0202 0101 1313 1615 0202 1307
Area2_19, 0707 1309 0909 0502 0202 0101 2009 1510 0202 0704
POP
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_3, 1313 1414 1212 0404 0202 0101 1909 1510 0202 0704
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_6, 1306 0909 1212 0202 0202 0101 0807 1512 0202 0707
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_9, 1515 0606 1212 0202 0202 0101 0909 1510 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_12, 1109 0902 1212 0404 0202 0101 0909 1002 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_15, 1307 1007 1412 0404 0505 0101 0909 0000 0202 0807
Area3_16, 1811 0000 1212 0404 0505 0000 1515 0707 0000 1212
Area3_17, 1907 1414 1512 0402 0705 0000 0000 0000 0909 1212
POP
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_3, 1509 2118 1212 0404 0505 0000 1515 0707 0707 1107
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_6, 1717 2222 1512 0404 0505 0000 1913 0707 0707 0907
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_9, 1311 2019 1512 0404 0505 0000 1515 0707 0707 1111
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_12, 0907 2724 0000 0404 0505 0605 1914 1105 0505 0707
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_15, 1311 1616 1212 0404 0505 0606 1313 1111 0707 1107
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_18, 1107 0904 1512 0404 0505 0606 1913 1105 1107 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_21, 1111 0606 1515 0404 0505 0606 1707 1009 0807 0502
Area4_22, 1311 0603 1512 0404 0505 0606 1714 0707 0807 0501
POP
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_3, 1518 0000 1512 0404 0707 0806 0909 0000 0909 1212
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_6, 1818 1414 1212 0404 0707 0000 1916 0000 0909 1208
Area5_7, 1318 1313 1212 0404 0606 0000 1908 0000 0808 1008
Area5_8, 1818 0000 1212 0404 0000 0806 1616 0000 0808 1212
Baseline data for trained clustering
1
2
3
4
5
6
7
8
9
10
POP
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_3, 0000 1010 1414 0404 0505 0606 1111 1212 0707 1212
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_6, 1111 1010 1414 0404 0505 0606 1111 1212 0707 1010
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_9, 0909 1010 1010 0404 0505 0606 1919 1212 0707 1010
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_12, 1515 1010 0606 0404 0505 0606 1616 1212 0202 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_15, 1111 1010 1414 0404 0505 0606 1616 0606 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_18, 1515 1010 1212 0404 0505 0606 0909 1212 0707 1010
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_21, 1515 1212 1212 0404 0505 0606 0808 1818 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_24, 0000 0000 0000 0404 0505 0606 1919 1212 0404 0000
Area1_25, 0000 0000 0000 0404 0202 0101 1919 1919 0707 1212
Area1_26, 1313 1010 0202 0404 0505 0606 1717 1212 0707 1212
POP
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_3, 1711 0000 1412 0402 0000 0000 1913 1002 0000 1313
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_6, 1309 0000 1212 0404 0603 0802 1311 1204 0903 1309
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_9, 0000 1212 1209 0404 0505 0606 0000 1106 0707 1208
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_12, 1807 0000 1212 0505 0606 0000 0909 0401 0909 1309
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_15, 1817 0707 1212 0505 0707 0909 1111 1004 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_18, 0707 0606 1408 0404 0202 0101 1313 1615 0202 1307
Area2_19, 0707 1309 0909 0502 0202 0101 2009 1510 0202 0704
POP
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_3, 1313 1414 1212 0404 0202 0101 1909 1510 0202 0704
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_6, 1306 0909 1212 0202 0202 0101 0807 1512 0202 0707
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_9, 1515 0606 1212 0202 0202 0101 0909 1510 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_12, 1109 0902 1212 0404 0202 0101 0909 1002 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_15, 1307 1007 1412 0404 0505 0101 0909 0000 0202 0807
Area3_16, 1811 0000 1212 0404 0505 0000 1515 0707 0000 1212
Area3_17, 1907 1414 1512 0402 0705 0000 0000 0000 0909 1212
POP
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_3, 1509 2118 1212 0404 0505 0000 1515 0707 0707 1107
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_6, 1717 2222 1512 0404 0505 0000 1913 0707 0707 0907
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_9, 1311 2019 1512 0404 0505 0000 1515 0707 0707 1111
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_12, 0907 2724 0000 0404 0505 0605 1914 1105 0505 0707
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_15, 1311 1616 1212 0404 0505 0606 1313 1111 0707 1107
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_18, 1107 0904 1512 0404 0505 0606 1913 1105 1107 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_21, 1111 0606 1515 0404 0505 0606 1707 1009 0807 0502
Area4_22, 1311 0603 1512 0404 0505 0606 1714 0707 0807 0501
POP
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_3, 1518 0000 1512 0404 0707 0806 0909 0000 0909 1212
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_6, 1818 1414 1212 0404 0707 0000 1916 0000 0909 1208
Area5_7, 1318 1313 1212 0404 0606 0000 1908 0000 0808 1008
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
3.1011410e+006 6.7108850e+006
3.1015790e+006 6.7101790e+006
3.1015910e+006 6.7100650e+006
3.1013610e+006 6.7104850e+006
3.1011410e+006 6.7108850e+006
3.1015790e+006 6.7101790e+006
3.1015910e+006 6.7100650e+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
3.1016310e+006 6.7101990e+006
3.1015790e+006 6.7101790e+006
3.1015910e+006 6.7100650e+006
3.1017660e+006 6.7104190e+006
3.1017500e+006 6.7106040e+006
3.1019250e+006 6.7092640e+006
3.1017430e+006 6.7094990e+006
3.1015240e+006 6.7097430e+006
0 0
3.1013610e+006 6.7104850e+006
3.1016310e+006 6.7101990e+006
3.1015790e+006 6.7101790e+006
3.1015910e+006 6.7100650e+006
3.1017660e+006 6.7104190e+006
3.1017500e+006 6.7106040e+006
3.1019250e+006 6.7092640e+006
3.1017430e+006 6.7094990e+006
3.1015240e+006 6.7097430e+006
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 54 82 102 92 116 90 86 104 1
88 104 56 84 102 -9 120 90 88 100 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 56 80 100 90 118 90 -9 104 3
80 102 54 82 102 92 116 90 86 104 4
88 104 56 84 102 92 120 90 88 100 4
86 102 56 80 -9 90 116 90 86 100 5
88 102 54 80 -9 90 116 92 86 100 5
-9 102 56 80 100 90 118 90 88 104 1
-9 102 54 82 102 92 116 90 86 104 1
88 104 56 84 102 -9 120 90 88 100 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 56 80 100 90 118 90 -9 104 3
80 102 54 82 102 92 116 90 86 104 4
88 104 56 84 102 92 120 90 88 100 4
86 102 56 80 -9 90 116 90 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
View file

@ -1,5 +1,5 @@
88 102 56 80 100 90 118 -9 88 104 1
80 102 54 82 102 92 116 90 86 104 2
88 104 56 84 102 -9 120 90 88 100 3
86 102 56 80 102 90 116 90 86 100 4
88 102 -9 80 102 90 116 92 86 100 5
88 102 56 80 100 90 118 -9 88 104 1
80 102 54 82 102 92 116 90 86 104 2
88 104 56 84 102 -9 120 90 88 100 3
86 102 56 80 102 90 116 90 86 100 4
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
80 102 54 82 102 92 116 90 86 104 1
88 102 56 80 100 90 118 90 88 104 1
80 102 54 82 102 92 116 90 86 104 1
88 -9 56 84 102 92 120 90 88 100 2
86 -9 56 80 102 90 116 90 86 102 2
88 -9 56 84 102 92 120 90 88 100 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 56 80 100 90 118 90 88 104 3
88 102 54 80 102 90 116 92 86 102 3
88 102 56 80 100 90 118 90 88 104 3
80 102 54 82 102 92 116 -9 86 104 4
88 104 56 84 102 92 120 -9 88 100 4
80 102 54 82 102 92 116 -9 86 104 4
88 104 56 84 102 92 120 -9 88 100 4
86 102 56 80 102 90 116 90 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
88 102 54 80 102 90 116 92 86 100 5
88 102 56 80 100 90 118 90 88 104 1
80 102 54 82 102 92 116 90 86 104 1
88 102 56 80 100 90 118 90 88 104 1
80 102 54 82 102 92 116 90 86 104 1
88 -9 56 84 102 92 120 90 88 100 2
86 -9 56 80 102 90 116 90 86 102 2
88 -9 56 84 102 92 120 90 88 100 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 56 80 100 90 118 90 88 104 3
88 102 54 80 102 90 116 92 86 102 3
88 102 56 80 100 90 118 90 88 104 3
80 102 54 82 102 92 116 -9 86 104 4
88 104 56 84 102 92 120 -9 88 100 4
80 102 54 82 102 92 116 -9 86 104 4
88 104 56 84 102 92 120 -9 88 100 4
86 102 56 80 102 90 116 90 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
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 54 82 102 92 116 90 86 104 1
88 104 56 84 102 -9 120 90 88 100 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 56 80 100 90 118 90 -9 104 2
80 102 54 82 102 92 116 90 86 104 2
88 104 56 84 102 92 120 90 88 100 2
86 102 56 80 -9 90 116 90 86 100 3
88 102 54 80 -9 90 116 92 86 100 3
-9 102 56 80 100 90 118 90 88 104 1
-9 102 54 82 102 92 116 90 86 104 1
88 104 56 84 102 -9 120 90 88 100 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 56 80 100 90 118 90 -9 104 2
80 102 54 82 102 92 116 90 86 104 2
88 104 56 84 102 92 120 90 88 100 2
86 102 56 80 -9 90 116 90 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
80 102 54 82 102 92 116 90 86 104 1
88 104 56 84 102 -9 120 90 88 100 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 56 80 100 90 118 -9 88 104 1
80 102 54 82 102 92 116 90 86 104 1
88 104 56 84 102 -9 120 90 88 100 2
86 102 56 80 102 90 116 90 86 100 2
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
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@ -1,20 +1,20 @@
88 102 56 80 100 90 118 90 88 104 1
80 102 54 82 102 92 116 90 86 104 1
88 102 56 80 100 90 118 90 88 104 1
80 102 54 82 102 92 116 90 86 104 1
88 -9 56 84 102 92 120 90 88 100 1
86 -9 56 80 102 90 116 90 86 102 1
88 -9 56 84 102 92 120 90 88 100 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 56 80 100 90 118 90 88 104 2
88 102 54 80 102 90 116 92 86 102 2
88 102 56 80 100 90 118 90 88 104 2
80 102 54 82 102 92 116 -9 86 104 2
88 104 56 84 102 92 120 -9 88 100 2
80 102 54 82 102 92 116 -9 86 104 2
88 104 56 84 102 92 120 -9 88 100 2
86 102 56 80 102 90 116 90 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
88 102 54 80 102 90 116 92 86 100 3
88 102 56 80 100 90 118 90 88 104 1
80 102 54 82 102 92 116 90 86 104 1
88 102 56 80 100 90 118 90 88 104 1
80 102 54 82 102 92 116 90 86 104 1
88 -9 56 84 102 92 120 90 88 100 1
86 -9 56 80 102 90 116 90 86 102 1
88 -9 56 84 102 92 120 90 88 100 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 56 80 100 90 118 90 88 104 2
88 102 54 80 102 90 116 92 86 102 2
88 102 56 80 100 90 118 90 88 104 2
80 102 54 82 102 92 116 -9 86 104 2
88 104 56 84 102 92 120 -9 88 100 2
80 102 54 82 102 92 116 -9 86 104 2
88 104 56 84 102 92 120 -9 88 100 2
86 102 56 80 102 90 116 90 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
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
AACGAAACGATCGCGTCACCGGAACGTTGTCCGTCTCGAATAGCACTGTGGGAACGTGTTTTACATTCGT
TAGTAACATGGTCAGCTGCTCATCCGTATT
>2
ATCAGCAAACGAGAAGTTGCAGAGGTCTTTGGTTTGAGCATTGCCCCCATACAATCGACTTCTGGCCTGG
AATGCACCACAAACATACCCCACAGGCTCG
>3
GCTTTTACTAAGGCCTATCGGATTCAACGTCACTAAGACTCGGCACTAACAGGCCGTTGTAAGCCGCTCT
GTCTGAGTATGGATGGTGGAGGCGGAGCCG
>4
ACCTGGACCTCTGTATTAACGGCTGTGATTCTGAGGGGGGTATCGCAGCGCACTTTCTAGCTATATCACG
CAAGGATAAAGTTCACCCATCACGTTGACC
>5
ACAATACGTCATCCACACCGCGCCTATGGAAGAATTTGCCCTTTCGGCGACAGCCCATGCTGTCAAGGAG
GTAACATAGCTACCAGGTCCCATTCCAGGA
>1
AACGAAACGATCGCGTCACCGGAACGTTGTCCGTCTCGAATAGCACTGTGGGAACGTGTTTTACATTCGT
TAGTAACATGGTCAGCTGCTCATCCGTATT
>2
ATCAGCAAACGAGAAGTTGCAGAGGTCTTTGGTTTGAGCATTGCCCCCATACAATCGACTTCTGGCCTGG
AATGCACCACAAACATACCCCACAGGCTCG
>3
GCTTTTACTAAGGCCTATCGGATTCAACGTCACTAAGACTCGGCACTAACAGGCCGTTGTAAGCCGCTCT
GTCTGAGTATGGATGGTGGAGGCGGAGCCG
>4
ACCTGGACCTCTGTATTAACGGCTGTGATTCTGAGGGGGGTATCGCAGCGCACTTTCTAGCTATATCACG
CAAGGATAAAGTTCACCCATCACGTTGACC
>5
ACAATACGTCATCCACACCGCGCCTATGGAAGAATTTGCCCTTTCGGCGACAGCCCATGCTGTCAAGGAG
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
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 1
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 2
GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 2
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 3
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 3
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 4
GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 4
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 1
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 1
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 2
GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 2
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 3
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 3
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 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
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 2
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 3
GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 4
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 1
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 2
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 3
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
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@ -1,16 +1,16 @@
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 1
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 1
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 1
GTTATTGACTCGGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 1
TATCTAC--GAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 2
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 2
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 2
GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 2
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 3
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 3
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 3
GTTATTGACTCGGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 3
TATCTAC--GAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 4
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 4
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 4
GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 4
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 1
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 1
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 1
GTTATTGACTCGGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 1
TATCTAC--GAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 2
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 2
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 2
GTTATCTAC--GGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 2
CTAACGCTTGAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 3
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 3
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 3
GTTATTGACTCGGTTGCTGCACTAACACCT-AGCTGAGATCGA-GGCGAAATGGGCGATAGCCACA 3
TATCTAC--GAGCTTATTGCTGCAGCGCAGAAAGTAGGTAAAACGTGTGCATTCGTTGATGCGGAA 4
GCACTTGACCCTATCTACGCTCAAAAGCTTGGTGTTGATATTGACGCTTTGCTTGTATCTCAACCT 4
GATACGGGTGAACAAGCTCTAGAAATTTGTGATGCACTGGCTCGTTCAGGTGCTATCGATGTTCTT 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
View file

@ -1,8 +1,8 @@
>RecA-1
CTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAACCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC
>RecA-2
CTAACGCTTGAGCTTATTGCTGCAGCACAGA-AGTGGGCAAAACGTGTGCATTCGTCGATGCCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC
>RecA-3
CTAACGC--GAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAACCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC
>RecA-4
CTAACGCTTGAGCTTATTGCTGCAGCACAGA-AGTGGGCAAAACGTGTGCATTCGTCGATGCCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC
>RecA-1
CTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAACCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC
>RecA-2
CTAACGCTTGAGCTTATTGCTGCAGCACAGA-AGTGGGCAAAACGTGTGCATTCGTCGATGCCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC
>RecA-3
CTAACGC--GAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAACCTAACGCTTGAGCTTATT-CTGCAGCCAAATTCGTATGAAAATTGGTGTAATGTTCGGTAAC
>RecA-4
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
View file

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
2
2
2
2
2
3
3
3
1
1
2
2
2
2
2
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
1 1A1 My. splendidone 1 1 1 1 1 1
2 1B1 A. dent 2 2 2 2 2 2
3 1B2 A. dent 3 3 3 3 3 3
4 1A5 D. gently 4 4 4 4 4 4
ST Isolate Species Adk GyrB Hsp60 Mdh Pgi RecA
1 1A1 My. splendidone 1 1 1 1 1 1
2 1B1 A. dent 2 2 2 2 2 2
3 1B2 A. dent 3 3 3 3 3 3
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
View file

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
View file

@ -1,3 +1,3 @@
1
3
5
1
3
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 2
Example population 1
Example population 2
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
1
2
3
4
5
6
7
8
9
10
POP
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_13, 1717 0303 1515 0404 0505 0606 1414 1010 0707 1212
POP
AreaUnknown_24, 1311 1205 1212 0302 0505 0606 1313 1005 0707 1107
AreaUnknown_25, 1306 0707 1212 0505 0603 0808 1109 1003 0000 0806
POP
AreaUnknown_36, 0909 0606 1212 0404 0606 0808 1717 1001 0909 1308
POP
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_49, 0907 0907 1212 0404 0202 0101 1908 0000 0202 1507
AreaUnknown_410, 1109 1918 1512 0505 0505 0000 1605 0706 0707 0505
Sample data for trained clustering
1
2
3
4
5
6
7
8
9
10
POP
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_13, 1717 0303 1515 0404 0505 0606 1414 1010 0707 1212
POP
AreaUnknown_24, 1311 1205 1212 0302 0505 0606 1313 1005 0707 1107
AreaUnknown_25, 1306 0707 1212 0505 0603 0808 1109 1003 0000 0806
POP
AreaUnknown_36, 0909 0606 1212 0404 0606 0808 1717 1001 0909 1308
POP
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_49, 0907 0907 1212 0404 0202 0101 1908 0000 0202 1507
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
1
2
3
4
5
6
7
8
9
10
POP
AreaUnknown1, 1717 1010 1717 0404 0505 0606 0808 1010 0707 1212
POP
AreaUnknown2, 1515 1212 1414 0404 0505 0606 1919 1010 0707 1414
POP
AreaUnknown3, 1717 0303 1515 0404 0505 0606 1414 1010 0707 1212
POP
AreaUnknown4, 1311 1205 1212 0302 0505 0606 1313 1005 0707 1107
POP
AreaUnknown5, 1306 0707 1212 0505 0603 0808 1109 1003 0000 0806
POP
AreaUnknown6, 0909 0606 1212 0404 0606 0808 1717 1001 0909 1308
POP
AreaUnknown7, 1507 1208 1212 0404 0202 0101 1915 1510 0202 0705
POP
AreaUnknown8, 1507 1313 1212 0202 0202 0101 1414 1510 0202 0704
POP
AreaUnknown9, 0907 0907 1212 0404 0202 0101 1908 0000 0202 1507
POP
AreaUnknown10, 1109 1918 1512 0505 0505 0000 1605 0706 0707 0505
Sample data for trained clustering
1
2
3
4
5
6
7
8
9
10
POP
AreaUnknown1, 1717 1010 1717 0404 0505 0606 0808 1010 0707 1212
POP
AreaUnknown2, 1515 1212 1414 0404 0505 0606 1919 1010 0707 1414
POP
AreaUnknown3, 1717 0303 1515 0404 0505 0606 1414 1010 0707 1212
POP
AreaUnknown4, 1311 1205 1212 0302 0505 0606 1313 1005 0707 1107
POP
AreaUnknown5, 1306 0707 1212 0505 0603 0808 1109 1003 0000 0806
POP
AreaUnknown6, 0909 0606 1212 0404 0606 0808 1717 1001 0909 1308
POP
AreaUnknown7, 1507 1208 1212 0404 0202 0101 1915 1510 0202 0705
POP
AreaUnknown8, 1507 1313 1212 0202 0202 0101 1414 1510 0202 0704
POP
AreaUnknown9, 0907 0907 1212 0404 0202 0101 1908 0000 0202 1507
POP
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
View file

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
View file

0
inst/ext/ExamplesDataFormatting/Notes.md → inst/testdata/ExamplesDataFormatting/Notes.md vendored
View file

216
inst/ext/GENEPOP_format_trained_clustering.gen → inst/testdata/datasets-for-future-releases/GENEPOP_format_trained_clustering.gen vendored
View file

@ -1,108 +1,108 @@
Baseline data for trained clustering
1
2
3
4
5
6
7
8
9
10
POP
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_3, 0000 1010 1414 0404 0505 0606 1111 1212 0707 1212
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_6, 1111 1010 1414 0404 0505 0606 1111 1212 0707 1010
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_9, 0909 1010 1010 0404 0505 0606 1919 1212 0707 1010
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_12, 1515 1010 0606 0404 0505 0606 1616 1212 0202 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_15, 1111 1010 1414 0404 0505 0606 1616 0606 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_18, 1515 1010 1212 0404 0505 0606 0909 1212 0707 1010
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_21, 1515 1212 1212 0404 0505 0606 0808 1818 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_24, 0000 0000 0000 0404 0505 0606 1919 1212 0404 0000
Area1_25, 0000 0000 0000 0404 0202 0101 1919 1919 0707 1212
Area1_26, 1313 1010 0202 0404 0505 0606 1717 1212 0707 1212
POP
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_3, 1711 0000 1412 0402 0000 0000 1913 1002 0000 1313
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_6, 1309 0000 1212 0404 0603 0802 1311 1204 0903 1309
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_9, 0000 1212 1209 0404 0505 0606 0000 1106 0707 1208
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_12, 1807 0000 1212 0505 0606 0000 0909 0401 0909 1309
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_15, 1817 0707 1212 0505 0707 0909 1111 1004 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_18, 0707 0606 1408 0404 0202 0101 1313 1615 0202 1307
Area2_19, 0707 1309 0909 0502 0202 0101 2009 1510 0202 0704
POP
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_3, 1313 1414 1212 0404 0202 0101 1909 1510 0202 0704
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_6, 1306 0909 1212 0202 0202 0101 0807 1512 0202 0707
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_9, 1515 0606 1212 0202 0202 0101 0909 1510 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_12, 1109 0902 1212 0404 0202 0101 0909 1002 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_15, 1307 1007 1412 0404 0505 0101 0909 0000 0202 0807
Area3_16, 1811 0000 1212 0404 0505 0000 1515 0707 0000 1212
Area3_17, 1907 1414 1512 0402 0705 0000 0000 0000 0909 1212
POP
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_3, 1509 2118 1212 0404 0505 0000 1515 0707 0707 1107
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_6, 1717 2222 1512 0404 0505 0000 1913 0707 0707 0907
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_9, 1311 2019 1512 0404 0505 0000 1515 0707 0707 1111
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_12, 0907 2724 0000 0404 0505 0605 1914 1105 0505 0707
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_15, 1311 1616 1212 0404 0505 0606 1313 1111 0707 1107
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_18, 1107 0904 1512 0404 0505 0606 1913 1105 1107 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_21, 1111 0606 1515 0404 0505 0606 1707 1009 0807 0502
Area4_22, 1311 0603 1512 0404 0505 0606 1714 0707 0807 0501
POP
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_3, 1518 0000 1512 0404 0707 0806 0909 0000 0909 1212
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_6, 1818 1414 1212 0404 0707 0000 1916 0000 0909 1208
Area5_7, 1318 1313 1212 0404 0606 0000 1908 0000 0808 1008
Area5_8, 1818 0000 1212 0404 0000 0806 1616 0000 0808 1212
Baseline data for trained clustering
1
2
3
4
5
6
7
8
9
10
POP
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_3, 0000 1010 1414 0404 0505 0606 1111 1212 0707 1212
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_6, 1111 1010 1414 0404 0505 0606 1111 1212 0707 1010
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_9, 0909 1010 1010 0404 0505 0606 1919 1212 0707 1010
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_12, 1515 1010 0606 0404 0505 0606 1616 1212 0202 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_15, 1111 1010 1414 0404 0505 0606 1616 0606 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_18, 1515 1010 1212 0404 0505 0606 0909 1212 0707 1010
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_21, 1515 1212 1212 0404 0505 0606 0808 1818 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_24, 0000 0000 0000 0404 0505 0606 1919 1212 0404 0000
Area1_25, 0000 0000 0000 0404 0202 0101 1919 1919 0707 1212
Area1_26, 1313 1010 0202 0404 0505 0606 1717 1212 0707 1212
POP
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_3, 1711 0000 1412 0402 0000 0000 1913 1002 0000 1313
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_6, 1309 0000 1212 0404 0603 0802 1311 1204 0903 1309
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_9, 0000 1212 1209 0404 0505 0606 0000 1106 0707 1208
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_12, 1807 0000 1212 0505 0606 0000 0909 0401 0909 1309
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_15, 1817 0707 1212 0505 0707 0909 1111 1004 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_18, 0707 0606 1408 0404 0202 0101 1313 1615 0202 1307
Area2_19, 0707 1309 0909 0502 0202 0101 2009 1510 0202 0704
POP
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_3, 1313 1414 1212 0404 0202 0101 1909 1510 0202 0704
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_6, 1306 0909 1212 0202 0202 0101 0807 1512 0202 0707
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_9, 1515 0606 1212 0202 0202 0101 0909 1510 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_12, 1109 0902 1212 0404 0202 0101 0909 1002 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_15, 1307 1007 1412 0404 0505 0101 0909 0000 0202 0807
Area3_16, 1811 0000 1212 0404 0505 0000 1515 0707 0000 1212
Area3_17, 1907 1414 1512 0402 0705 0000 0000 0000 0909 1212
POP
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_3, 1509 2118 1212 0404 0505 0000 1515 0707 0707 1107
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_6, 1717 2222 1512 0404 0505 0000 1913 0707 0707 0907
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_9, 1311 2019 1512 0404 0505 0000 1515 0707 0707 1111
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_12, 0907 2724 0000 0404 0505 0605 1914 1105 0505 0707
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_15, 1311 1616 1212 0404 0505 0606 1313 1111 0707 1107
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_18, 1107 0904 1512 0404 0505 0606 1913 1105 1107 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_21, 1111 0606 1515 0404 0505 0606 1707 1009 0807 0502
Area4_22, 1311 0603 1512 0404 0505 0606 1714 0707 0807 0501
POP
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_3, 1518 0000 1512 0404 0707 0806 0909 0000 0909 1212
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_6, 1818 1414 1212 0404 0707 0000 1916 0000 0909 1208
Area5_7, 1318 1313 1212 0404 0606 0000 1908 0000 0808 1008
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}
\title{Clustering of individuals}
\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{
\item{data}{data file}
\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}
}
\description{
Clustering of individuals
}
\examples{
data <- system.file("extdata", "FASTA_clustering_haploid.fasta", package = "rBAPS")
greedyMix(data, "fasta")
}
\references{
Samtools: a suite of programs for interacting
with high-throughput sequencing data. <http://www.htslib.org/>

8
man/handleData.Rd
View file

@ -4,10 +4,12 @@
\alias{handleData}
\title{Handle Data}
\usage{
handleData(raw_data)
handleData(raw_data, format = "Genepop")
}
\arguments{
\item{raw_data}{Raw data}
\item{raw_data}{Raw data in Genepop or BAPS format}
\item{format}{data format}
}
\description{
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
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
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}
\title{load_fasta}
\usage{
load_fasta(msa, keep.singletons = FALSE)
load_fasta(msa, keep_singletons = FALSE, output_numbers = TRUE)
}
\arguments{
\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{
A character matrix with filtered SNP data
@ -19,8 +22,8 @@ Loads a fasta file into matrix format ready for
running the hierBAPS algorithm.
}
\examples{
msa <- system.file("ext", "seqs.fa", package = "rBAPS")
snp.matrix <- load_fasta(msa)
msa <- system.file("extdata", "seqs.fa", package = "rBAPS")
snp.matrix <- rBAPS:::load_fasta(msa)
}
\seealso{
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
}
\examples{
randdir(matrix(c(10, 30, 60), 3), 3)
rBAPS:::randdir(matrix(c(10, 30, 60), 3), 3)
}
\seealso{
randga

70
matlab/independent/indMix.m
View file

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

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

@ -1,11 +1,11 @@
context("Auxiliary functions to greedyMix")
# Defining the relative path to current inst -----------------------------------
path_inst <- system.file("ext", "", package = "rBAPS")
path_inst <- system.file("extdata", "", package = "rBAPS")
# Reading datasets -------------------------------------------------------------
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 = " ",
header = FALSE
)
@ -31,26 +31,27 @@ test_that("handleData works as expected", {
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"),
format = "FASTA"
)
df_vcf <- greedyMix(
raw_vcf <- importFile(
data = file.path(path_inst, "vcf_example.vcf"),
format = "VCF",
verbose = FALSE
)
df_bam <- greedyMix(
df_bam <- importFile(
data = file.path(path_inst, "bam_example.bam"),
format = "BAM",
)
test_that("Files are imported correctly", {
expect_equal(dim(df_fasta), c(5, 99))
expect_equal(dim(df_vcf), c(variants = 2, fix_cols = 8, gt_cols = 3))
expect_equal(dim(raw_fasta), c(5, 99))
expect_equal(dim(raw_vcf), c(variants = 2, fix_cols = 8, gt_cols = 3))
expect_error(
greedyMix(
importFile(
data = paste(path_inst, "sam_example.sam", sep = "/"),
format = "SAM",
)
@ -58,6 +59,15 @@ test_that("Files are imported correctly", {
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")
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", {
msa <- system.file("ext", "seqs.fa", package = "rBAPS")
msa <- system.file("extdata", "seqs.fa", package = "rBAPS")
test_msa <- testFastaData(msa)
expect_equal(test_msa$ninds, 515)
expect_equal(dim(test_msa$data), c(515, 745))