Fixed basic parsing of FASTA files (#25)
This commit is contained in:
Waldir Leoncio
parent
a88f31b3a5
commit
76828387a3
8 changed files with 80 additions and 78 deletions
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@ -1,6 +1,7 @@
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# Generated by roxygen2: do not edit by hand
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export(greedyMix)
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export(handleData)
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export(load_fasta)
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importFrom(R6,R6Class)
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importFrom(Rsamtools,scanBam)
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@ -54,7 +54,7 @@ greedyMix <- function(
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# Generating partition summary ===============================================
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ekat <- seq(1L, c[["rowsFromInd"]], ninds * c[["rowsFromInd"]]) # ekat = (1:rowsFromInd:ninds*rowsFromInd)';
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c[["rows"]] <- c(ekat, ekat + c[["rowsFromInd"]] - 1L) # c.rows = [ekat ekat+rowsFromInd-1]
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logml_npops_partitionSummary <- indMixWrapper(c, npops, counts, sumcounts, max_iter, fixedK, verbose);
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logml_npops_partitionSummary <- indMixWrapper(c, npops, counts, sumcounts, max_iter, fixedK, verbose)
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logml <- logml_npops_partitionSummary[["logml"]]
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npops <- logml_npops_partitionSummary[["npops"]]
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partitionSummary <- logml_npops_partitionSummary[["partitionSummary"]]
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@ -72,8 +72,8 @@ greedyMix <- function(
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# Writing mixture info =======================================================
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changesInLogml <- writeMixtureInfo(
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logml, rowsFromInd, data, adjprior, priorTerm, NULL, inp, partitionSummary,
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popnames, fixedK
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logml, c[["rowsFromInd"]], c[["data"]], c[["adjprior"]], c[["priorTerm"]],
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NULL, inp, partitionSummary, popnames, fixedK
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)
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# Updateing results ==========================================================
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@ -9,6 +9,7 @@
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#' code to the smallest code that is larger than any code in use. After this,
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#' the function changes the allele codes so that one locus j
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#' codes get values between? 1, ..., noalle(j).
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#' @export
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handleData <- function(raw_data, format = "Genepop") {
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# Alkuper?isen datan viimeinen sarake kertoo, milt?yksil?lt?
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# kyseinen rivi on per?isin. Funktio tutkii ensin, ett?montako
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@ -68,7 +68,7 @@ indMix <- function(c, npops, counts = NULL, sumcounts = NULL, max_iter = 100L, d
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nruns <- length(npopsTaulu)
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initData <- data
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data <- data[, 1:(ncol(data) - 1)]
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data <- data[, seq_along(noalle)] # Original code always dropped last column.
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logmlBest <- -1e50
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partitionSummary <- -1e50 * ones(30, 2) # Tiedot 30 parhaasta partitiosta (npops ja logml)
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@ -1,7 +1,7 @@
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laskeLoggis <- function(counts, sumcounts, adjprior) {
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npops <- size(counts, 3)
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sum1 <- sum(sum(sum(lgamma(counts + repmat(adjprior, c(1, 1, npops))))))
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replicated_adjprior <- array(adjprior, c(nrow(adjprior), ncol(adjprior), npops))
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sum1 <- sum(sum(sum(lgamma(counts + replicated_adjprior))))
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sum3 <- sum(sum(lgamma(adjprior))) - sum(sum(lgamma(1 + sumcounts)))
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logml2 <- sum1 - npops * sum3
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loggis <- logml2
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@ -31,12 +31,12 @@ writeMixtureInfo <- function(
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}
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dispLine()
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cat("RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:")
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cat(c("Data file: ", inputFile))
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cat("Model: independent")
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cat(c("Number of clustered individuals: ", ownNum2Str(ninds)))
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cat(c("Number of groups in optimal partition: ", ownNum2Str(npops)))
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cat(c("Log(marginal likelihood) of optimal partition: ", ownNum2Str(logml)))
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cat("RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:\n")
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cat("Data file: ", inputFile, "\n")
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cat("Model: independent\n")
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cat("Number of clustered individuals: ", ownNum2Str(ninds), "\n")
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cat("Number of groups in optimal partition: ", ownNum2Str(npops), "\n")
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cat("Log(marginal likelihood) of optimal partition: ", ownNum2Str(logml), "\n")
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cat(" ")
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if (fid != -1) {
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append(fid, "RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:\n")
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@ -87,10 +87,10 @@ writeMixtureInfo <- function(
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"Cluster ", as.character(m), ": {", as.character(indsInM[1])
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)
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for (k in 2:cluster_size) {
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text <- c(text, ", ", as.character(indsInM[k]))
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text <- c(text, ",", as.character(indsInM[k]))
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}
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}
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text <- c(text, "}")
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text <- c(text, "}\n")
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while (length(text) > 58) {
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# Take one line and display it.
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new_line <- takeLine(text, 58)
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@ -106,7 +106,7 @@ writeMixtureInfo <- function(
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text <- ""
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}
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}
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if (text != "") {
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if (any(text != "")) {
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cat(text)
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if (fid != -1) {
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append(fid, text)
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@ -116,11 +116,11 @@ writeMixtureInfo <- function(
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}
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if (npops > 1) {
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cat(" ")
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cat(" ")
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cat("\n")
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cat("\n")
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cat(
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"Changes in log(marginal likelihood)",
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" if indvidual i is moved to group j:"
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" if indvidual i is moved to group j:\n"
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)
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if (fid != -1) {
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append(fid, " ")
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@ -131,7 +131,7 @@ writeMixtureInfo <- function(
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fid,
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c(
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"Changes in log(marginal likelihood)",
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"if indvidual i is moved to group j:"
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"if indvidual i is moved to group j:\n"
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)
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)
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append(fid, "\n")
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@ -167,9 +167,9 @@ writeMixtureInfo <- function(
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if (names) {
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nimi <- as.character(popnames[ind])
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rivi <- c(blanks(maxSize - length(nimi)), nimi, ":")
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rivi <- c(blanks(maxSize - length(nimi)), nimi, ":\n")
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} else {
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rivi <- c(blanks(4 - floor(log10(ind))), ownNum2Str(ind), ":")
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rivi <- c("\n", blanks(4 - floor(log10(ind))), ownNum2Str(ind), ":\n")
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}
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for (j in 1:npops) {
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rivi <- c(rivi, " ", logml2String(omaRound(muutokset[j])))
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@ -181,9 +181,9 @@ writeMixtureInfo <- function(
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}
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}
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cat(" ")
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cat(" ")
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cat("KL-divergence matrix in PHYLIP format:")
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cat("\n")
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cat("\n")
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cat("KL-divergence matrix in PHYLIP format:\n")
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dist_mat <- zeros(npops, npops)
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if (fid != -1) {
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@ -193,6 +193,7 @@ writeMixtureInfo <- function(
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append(fid, "\n")
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}
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COUNTS <- COUNTS[seq_len(nrow(adjprior)), seq_len(ncol(adjprior)), , drop = FALSE]
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maxnoalle <- size(COUNTS, 1)
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nloci <- size(COUNTS, 2)
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d <- zeros(maxnoalle, nloci, npops)
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@ -204,8 +205,8 @@ writeMixtureInfo <- function(
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prior[1, nollia] <- 1
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for (pop1 in 1:npops) {
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d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) /
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repmat(sum(squeeze(COUNTS[, , pop1]) + prior), c(maxnoalle, 1))
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squeezed_COUNTS_prior <- squeeze(COUNTS[, , pop1]) + prior
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d[, , pop1] <- squeezed_COUNTS_prior / sum(squeezed_COUNTS_prior)
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}
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ekarivi <- as.character(npops)
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cat(ekarivi)
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@ -215,14 +216,14 @@ writeMixtureInfo <- function(
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}
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for (pop1 in 1:npops) {
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for (pop2 in 1:(pop1 - 1)) {
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for (pop2 in seq_len(pop1 - 1)) {
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dist1 <- d[, , pop1]
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dist2 <- d[, , pop2]
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div12 <- sum(
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sum(dist1 * log2((dist1 + 10^-10) / (dist2 + 10^-10)))
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sum(dist1 * base::log2((dist1 + 10^-10) / (dist2 + 10^-10)))
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) / nloci
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div21 <- sum(
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sum(dist2 * log2((dist2 + 10^-10) / (dist1 + 10^-10)))
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sum(dist2 * base::log2((dist2 + 10^-10) / (dist1 + 10^-10)))
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) / nloci
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div <- (div12 + div21) / 2
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dist_mat[pop1, pop2] <- div
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@ -232,9 +233,9 @@ writeMixtureInfo <- function(
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dist_mat <- dist_mat + t(dist_mat) # make it symmetric
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for (pop1 in 1:npops) {
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rivi <- c("Cluster_", as.character(pop1), " ")
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rivi <- c("\nCluster_", as.character(pop1), "\n")
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for (pop2 in 1:npops) {
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rivi <- c(rivi, kldiv2str(dist_mat[pop1, pop2]), " ")
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rivi <- c(rivi, kldiv2str(dist_mat[pop1, pop2]))
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}
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cat(rivi)
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if (fid != -1) {
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@ -244,11 +245,11 @@ writeMixtureInfo <- function(
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}
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}
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cat(" ")
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cat(" ")
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cat("\n")
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cat("\n")
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cat(
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"List of sizes of 10 best visited partitions",
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"and corresponding log(ml) values"
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"and corresponding log(ml) values\n"
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)
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if (fid != -1) {
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@ -278,7 +279,7 @@ writeMixtureInfo <- function(
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line <- c(
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as.character(partitionSummary[part, 1]),
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" ",
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as.character(partitionSummary(part, 2))
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as.character(partitionSummary[part, 2])
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)
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cat(line)
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if (fid != -1) {
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@ -288,9 +289,9 @@ writeMixtureInfo <- function(
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}
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if (!fixedK) {
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cat(" ")
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cat(" ")
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cat("Probabilities for number of clusters")
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cat("\n")
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cat("\n")
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cat("Probabilities for number of clusters\n")
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if (fid != -1) {
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append(fid, " ")
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@ -322,7 +323,7 @@ writeMixtureInfo <- function(
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line <- c(
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as.character(npopsTaulu[i]), " ", as.character(probs[i])
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)
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cat(line)
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cat(line, "\n")
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if (fid != -1) {
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append(fid, line)
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append(fid, "\n")
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@ -8,9 +8,8 @@ greedyMix(
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data,
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format,
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partitionCompare = NULL,
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ninds = NULL,
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ninds = 1L,
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npops = 1L,
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priorTerm = NULL,
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counts = NULL,
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sumcounts = NULL,
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max_iter = 100L,
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@ -32,8 +31,6 @@ greedyMix(
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\item{npops}{number of populations}
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\item{priorTerm}{prior terms}
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\item{counts}{counts}
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\item{sumcounts}{sumcounts}
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@ -55,6 +52,8 @@ greedyMix(
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\item{noalle}{number of alleles}
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\item{adjprior}{ajuster prior probabilities}
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\item{priorTerm}{prior terms}
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}
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\description{
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Clustering of individuals
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@ -87,7 +87,7 @@ for run = 1:nruns
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apu = rows(i);
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PARTITION(i) = initialPartition(apu(1));
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end
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COUNTS = counts; SUMCOUNTS = sumcounts;
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POP_LOGML = computePopulationLogml(1:npops, adjprior, priorTerm);
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LOGDIFF = repmat(-Inf,ninds,npops);
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@ -98,7 +98,7 @@ for run = 1:nruns
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kokeiltu = zeros(nRoundTypes, 1);
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roundTypes = [1 1]; %Ykkösvaiheen sykli kahteen kertaan.
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ready = 0; vaihe = 1;
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if dispText
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disp(' ');
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disp(['Mixture analysis started with initial ' num2str(npops) ' populations.']);
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@ -106,11 +106,11 @@ for run = 1:nruns
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while ready ~= 1
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muutoksia = 0;
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if dispText
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disp(['Performing steps: ' num2str(roundTypes)]);
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end
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for n = 1:length(roundTypes)
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round = roundTypes(n);
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@ -465,7 +465,7 @@ for run = 1:nruns
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npops = poistaTyhjatPopulaatiot(npops);
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POP_LOGML = computePopulationLogml(1:npops, adjprior, priorTerm);
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if dispText
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if dispText
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disp(['Found partition with ' num2str(npops) ' populations.']);
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disp(['Log(ml) = ' num2str(logml)]);
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disp(' ');
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@ -491,7 +491,7 @@ COUNTS = countsBest;
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SUMCOUNTS = sumCountsBest;
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POP_LOGML = pop_logmlBest;
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LOGDIFF = logdiffbest;
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%--------------------------------------------------------------------------
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function clearGlobalVars
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@ -509,9 +509,9 @@ function Z = linkage(Y, method)
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[k, n] = size(Y);
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m = (1+sqrt(1+8*n))/2;
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if k ~= 1 | m ~= fix(m)
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error('The first input has to match the output of the PDIST function in size.');
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error('The first input has to match the output of the PDIST function in size.');
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end
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if nargin == 1 % set default switch to be 'co'
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if nargin == 1 % set default switch to be 'co'
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method = 'co';
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end
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method = lower(method(1:2)); % simplify the switch string.
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@ -519,19 +519,19 @@ monotonic = 1;
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Z = zeros(m-1,3); % allocate the output matrix.
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N = zeros(1,2*m-1);
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N(1:m) = 1;
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n = m; % since m is changing, we need to save m in n.
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n = m; % since m is changing, we need to save m in n.
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R = 1:n;
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for s = 1:(n-1)
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X = Y;
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[v, k] = min(X);
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i = floor(m+1/2-sqrt(m^2-m+1/4-2*(k-1)));
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j = k - (i-1)*(m-i/2)+i;
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Z(s,:) = [R(i) R(j) v]; % update one more row to the output matrix A
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Z(s,:) = [R(i) R(j) v]; % update one more row to the output matrix A
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I1 = 1:(i-1); I2 = (i+1):(j-1); I3 = (j+1):m; % these are temp variables.
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U = [I1 I2 I3];
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I = [I1.*(m-(I1+1)/2)-m+i i*(m-(i+1)/2)-m+I2 i*(m-(i+1)/2)-m+I3];
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J = [I1.*(m-(I1+1)/2)-m+j I2.*(m-(I2+1)/2)-m+j j*(m-(j+1)/2)-m+I3];
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switch method
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case 'si' %single linkage
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Y(I) = min(Y(I),Y(J));
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@ -548,12 +548,12 @@ for s = 1:(n-1)
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end
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J = [J i*(m-(i+1)/2)-m+j];
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Y(J) = []; % no need for the cluster information about j.
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% update m, N, R
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m = m-1;
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m = m-1;
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N(n+s) = N(R(i)) + N(R(j));
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R(i) = n+s;
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R(j:(n-1))=R((j+1):n);
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R(j:(n-1))=R((j+1):n);
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end
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@ -623,7 +623,7 @@ function [muutokset, diffInCounts] = ...
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%
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% Lisäys 25.9.2007:
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% Otettu käyttöön globaali muuttuja LOGDIFF, johon on tallennettu muutokset
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% logml:ssä siirrettäessä yksilöitä toisiin populaatioihin.
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% logml:ssä siirrettäessä yksilöitä toisiin populaatioihin.
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global COUNTS; global SUMCOUNTS;
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global PARTITION; global POP_LOGML;
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@ -647,7 +647,7 @@ COUNTS(:,:,i1) = COUNTS(:,:,i1)+diffInCounts;
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SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)+diffInSumCounts;
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i2 = find(muutokset==-Inf); % Etsitään populaatiot jotka muuttuneet viime kerran jälkeen.
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i2 = setdiff(i2,i1);
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i2 = setdiff(i2,i1);
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i2_logml = POP_LOGML(i2);
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ni2 = length(i2);
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@ -668,19 +668,19 @@ LOGDIFF(ind,:) = muutokset;
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function diffInCounts = computeDiffInCounts(rows, max_noalle, nloci, data)
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% Muodostaa max_noalle*nloci taulukon, jossa on niiden alleelien
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% lukumäärät (vastaavasti kuin COUNTS:issa), jotka ovat data:n
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% lukumäärät (vastaavasti kuin COUNTS:issa), jotka ovat data:n
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% riveillä rows. rows pitää olla vaakavektori.
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diffInCounts = zeros(max_noalle, nloci);
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for i=rows
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row = data(i,:);
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notEmpty = find(row>=0);
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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
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue