From faa0a207156e9d575dcbb61d774d16e6d24f9371 Mon Sep 17 00:00:00 2001 From: Christian Mertes Date: Thu, 27 Aug 2020 11:41:29 -0700 Subject: [PATCH] FRASER revision (#13) * update and adjust `injectOutlier` and `hyperParameter` functions * option to compute z scores in logit space or not * add cap value [0.01,0.99] to logit function * bugfixes (withDimnames, dirname in save function, non existing chromosomes) * dont force HDF5 * proper pairedEnd counting Co-authored-by: Ines Scheller --- .travis.yml | 3 +- DESCRIPTION | 1 - NAMESPACE | 1 + R/beta-binomial-testing.R | 4 +- R/calculatePSIValue.R | 100 ++-- R/calculateStats.R | 53 -- R/countRNAseqData.R | 17 +- R/find_encoding_dimensions.R | 16 +- R/fitCorrectionMethods.R | 15 +- R/getNSetterFuns.R | 12 +- R/makeSimulatedDataset.R | 733 ++++++++++++----------- R/plotMethods.R | 4 +- R/pvalsNzscore.R | 5 +- R/saveHDF5Objects.R | 16 +- R/updateRho.R | 3 +- appveyor.yml | 9 +- inst/extdata/bam/sample1.bam | Bin 53337 -> 49872 bytes inst/extdata/bam/sample1.bam.bai | Bin 63048 -> 62296 bytes inst/extdata/bam/sample2.bam | Bin 236709 -> 235003 bytes inst/extdata/bam/sample2.bam.bai | Bin 62960 -> 62304 bytes inst/extdata/bam/sample3.bam | Bin 185537 -> 183840 bytes inst/extdata/bam/sample3.bam.bai | Bin 63072 -> 62416 bytes inst/extdata/sampleTable.tsv | 8 +- man/injectOutliers.Rd | 9 +- man/makeSimulatedFraserDataSet.Rd | 44 ++ man/optimHyperParams.Rd | 10 +- setupEnv.R | 4 + src/loss_n_gradient_functions.cpp | 2 +- tests/testthat.R | 5 + tests/testthat/test_counting.R | 22 +- tests/testthat/test_fraser_pipeline.R | 9 - tests/testthat/test_getter_setter.R | 14 +- tests/testthat/test_hyperParams.R | 59 +- tests/testthat/test_stats.R | 50 +- tests/testthat/test_write_read_objects.R | 32 + vignettes/FRASER.Rnw | 31 +- 36 files changed, 679 insertions(+), 612 deletions(-) delete mode 100644 R/calculateStats.R create mode 100644 man/makeSimulatedFraserDataSet.Rd create mode 100644 tests/testthat/test_write_read_objects.R diff --git a/.travis.yml b/.travis.yml index 66d28544..4dc6e333 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,6 +1,7 @@ language: r sudo: required -dist: bionic +# due to https://travis-ci.community/t/r-install-broken-travis-ci-rstudio-org-returns-403/9640/2 +# dist: bionic cache: - directories: - $HOME/R/Library diff --git a/DESCRIPTION b/DESCRIPTION index c27ecfdb..95d4b906 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -89,7 +89,6 @@ Collate: annotationOfRanges.R beta-binomial-testing.R calculatePSIValue.R - calculateStats.R countRNAseqData.R example_functions.R filterExpression.R diff --git a/NAMESPACE b/NAMESPACE index 6f01cfd7..7c317fb6 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -48,6 +48,7 @@ export(getSplitReadCountsForAllSamples) export(hyperParams) export(injectOutliers) export(loadFraserDataSet) +export(makeSimulatedFraserDataSet) export(mapSeqlevels) export(mergeCounts) export(name) diff --git a/R/beta-binomial-testing.R b/R/beta-binomial-testing.R index db402c50..c74ce1a2 100644 --- a/R/beta-binomial-testing.R +++ b/R/beta-binomial-testing.R @@ -140,7 +140,7 @@ pvalueByBetaBinomialPerType <- function(fds, aname, psiType, pvalFun, pvalues_full[toTest,] <- pvalues # transform it to a hdf5 assay and save it to the dataset - assays(fds, type=psiType)[[aname]] <- pvalues_full + assay(fds, aname, type=psiType, withDimnames=FALSE) <- pvalues_full # save the pvalue calculations in the SE ranged object return(fds) @@ -201,7 +201,7 @@ betabinVglmTest <- function(cMat, alternative="two.sided", #' #' error/warning catching functions by martin morgan -#' http://stackoverflow.com/questions/4948361/how-do-i-save-warnings-and-errors-as-output-from-a-function +#' http://stackoverflow.com/questions/4948361 #' #' @noRd tryCatchFactory <- function(fun, timeout=300){ diff --git a/R/calculatePSIValue.R b/R/calculatePSIValue.R index 43ced501..af375985 100644 --- a/R/calculatePSIValue.R +++ b/R/calculatePSIValue.R @@ -36,18 +36,12 @@ calculatePSIValues <- function(fds, types=psiTypes, overwriteCts=FALSE, overwriteCts=overwriteCts, BPPARAM=BPPARAM) } - # save Fraser object to disk - fds <- saveFraserDataSet(fds) - # calculate the delta psi value for(psiType in types){ assayName <- paste0("delta_", psiType) fds <- calculateDeltaPsiValue(fds, psiType, assayName) } - # save final Fraser object to disk - fds <- saveFraserDataSet(fds) - # return it return(fds) } @@ -88,16 +82,10 @@ calculatePSIValuePrimeSite <- function(fds, psiType, overwriteCts, BPPARAM){ # check if other counts and psi values chache file exists already cacheFile <- getOtherCountsCacheFile(sample, fds) - if(file.exists(cacheFile)){ - h5 <- HDF5Array(filepath=cacheFile, name=h5DatasetName) - if((isFALSE(overwriteCts) | - !all(paste0("rawOtherCounts_psi", c(5, 3)) %in% - assayNames(fds)) ) && - nrow(h5) == nrow(K(fds, type="psi5"))){ - - return(h5) - } - unlink(cacheFile) + ans <- checkPsiCacheFile(cFile=cacheFile, dName=h5DatasetName, + overwrite=overwriteCts, ptype=c("psi5", "psi3"), fds=fds) + if(!is.null(ans)){ + return(ans) } # add sample specific counts to the data.table (K) @@ -128,41 +116,63 @@ calculatePSIValuePrimeSite <- function(fds, psiType, overwriteCts, BPPARAM){ countData[is.na(psi5),psi5:=1] countData[is.na(psi3),psi3:=1] + # if no HDF5 is requested return it as matrix + if(dontWriteHDF5(fds)){ + return(as.matrix(countData[,.(o5,o3,psi5,psi3)])) + } + # write other counts and psi values to h5 file - # get defind chunk sizes + # get defined chunk sizes chunkDims <- c( min(nrow(countData), options()[['FRASER-hdf5-chunk-nrow']]), 1) writeHDF5Array(as.matrix(countData[,.(o5,o3,psi5,psi3)]), - filepath=cacheFile, name=h5DatasetName, + filepath=cacheFile, name=h5DatasetName, chunkdim=chunkDims, level=7, verbose=FALSE) # get counts as DelayedMatrix HDF5Array(filepath=cacheFile, name=h5DatasetName) - } ) names(psiValues) <- samples(fds) # merge it and assign it to our object - assay(fds, type="j", "psi5", withDimnames=FALSE) <- - do.call(cbind, mapply('[', psiValues, TRUE, 3, drop=FALSE)) - assay(fds, type="j", "psi3", withDimnames=FALSE) <- - do.call(cbind, mapply('[', psiValues, TRUE, 4, drop=FALSE)) + assay(fds, type="j", "psi5", withDimnames=FALSE) <- do.call(cbind, + mapply('[', psiValues, TRUE, 3, drop=FALSE, SIMPLIFY=FALSE)) + assay(fds, type="j", "psi3", withDimnames=FALSE) <- do.call(cbind, + mapply('[', psiValues, TRUE, 4, drop=FALSE, SIMPLIFY=FALSE)) if(isTRUE(overwriteCts)){ assay(fds, type="j", "rawOtherCounts_psi5", withDimnames=FALSE) <- - do.call(cbind, bplapply(psiValues, BPPARAM=BPPARAM, - function(x){ x[,1,drop=FALSE] })) + do.call(cbind, mapply('[', psiValues, TRUE, 1, + drop=FALSE, SIMPLIFY=FALSE)) assay(fds, type="j", "rawOtherCounts_psi3", withDimnames=FALSE) <- - do.call(cbind, bplapply(psiValues, BPPARAM=BPPARAM, - function(x){ x[,2,drop=FALSE] })) + do.call(cbind, mapply('[', psiValues, TRUE, 2, + drop=FALSE, SIMPLIFY=FALSE)) } return(fds) } +#' +#' Helper function to check for PSI value cached data +#' +#' @noRd +checkPsiCacheFile <- function(cFile, dName, overwrite, ptype, fds){ + if(file.exists(cFile) && dName %in% h5ls(cFile)$name){ + h5 <- HDF5Array(filepath=cFile, name=dName) + aNames <- paste0("rawOtherCounts_", ptype) + if(isFALSE(overwrite) && all( aNames %in% assayNames(fds)) && + nrow(h5) == nrow(K(fds, type=ptype[1]))){ + return(h5) + } + h5delete(cFile, name=dName) + } + return(NULL) +} + + #' #' This function calculates the site PSI values for each splice site #' based on the FraserDataSet object @@ -206,16 +216,10 @@ calculateSitePSIValue <- function(fds, overwriteCts, BPPARAM){ # get counts and psiSite values from cache file if it exists cacheFile <- getOtherCountsCacheFile(sample, fds) - if(file.exists(cacheFile) && - psiH5datasetName %in% h5ls(cacheFile)$name){ - h5 <- HDF5Array(filepath=cacheFile, name=psiH5datasetName) - if((isFALSE(overwriteCts) | !psiROCName %in% assayNames(fds)) - && nrow(h5) == nrow(K(fds, type="psiSite"))){ - - return(h5) - } else{ - h5delete(cacheFile, name=psiH5datasetName) - } + ans <- checkPsiCacheFile(cFile=cacheFile, dName=psiH5datasetName, + overwrite=overwriteCts, ptype="psiSite", fds=fds) + if(!is.null(ans)){ + return(ans) } # add sample specific counts to the data.table @@ -226,7 +230,7 @@ calculateSitePSIValue <- function(fds, overwriteCts, BPPARAM){ sdata[,os:=sum(k)-k, by="spliceSiteID"] # remove the junction part since we only want to calculate the - # psi values for the splice sites themselfs + # psi values for the splice sites themselves sdata <- sdata[type=="spliceSite"] # calculate psi value @@ -235,8 +239,13 @@ calculateSitePSIValue <- function(fds, overwriteCts, BPPARAM){ # if psi is NA this means there were no reads at all so set it to 1 sdata[is.na(psiValue),psiValue:=1] + # if no HDF5 is requested return it as matrix + if(dontWriteHDF5(fds)){ + return(as.matrix(sdata[,.(os, psiValue)])) + } + # write other counts and psi values to h5 file - # get defind chunk sizes + # get defined chunk sizes chunkDims <- c( min(nrow(sdata), options()[['FRASER-hdf5-chunk-nrow']]), 2) @@ -251,12 +260,13 @@ calculateSitePSIValue <- function(fds, overwriteCts, BPPARAM){ names(psiSiteValues) <- samples(fds) # merge it and assign it to our object - assay(fds, type="ss", psiName, withDimnames=FALSE) <- - do.call(cbind, mapply('[', psiSiteValues, TRUE, 2, drop=FALSE)) + assay(fds, type="ss", psiName, withDimnames=FALSE) <- do.call(cbind, + mapply('[', psiSiteValues, TRUE, 2, drop=FALSE, + SIMPLIFY=FALSE)) if(isTRUE(overwriteCts)){ - assay(fds, type="ss", psiROCName, withDimnames=FALSE) <- - do.call(cbind, bplapply(psiSiteValues, BPPARAM=BPPARAM, - function(x) { x[,1,drop=FALSE] })) + assay(fds, type="ss", psiROCName, withDimnames=FALSE) <- do.call(cbind, + mapply('[', psiSiteValues, TRUE, 1, drop=FALSE, + SIMPLIFY=FALSE)) } return(fds) @@ -310,4 +320,4 @@ getOtherCountsCacheFolder <- function(fds){ # return it return(cachedir) -} \ No newline at end of file +} diff --git a/R/calculateStats.R b/R/calculateStats.R deleted file mode 100644 index 669ac009..00000000 --- a/R/calculateStats.R +++ /dev/null @@ -1,53 +0,0 @@ -## -## @author Christian Mertes \email{mertes@@in.tum.de} -## -## This file contains all functions for calculating the statistics -## First it starts with calculating the Z-score for each -## site and then the p-values are calculated dependend on the -## given method in the setting file -## - -#' -#' Calculate the zscore for each PSI value. -#' -#' @noRd -#' @return FraserDataSet -#' @examples -#' fds <- createTestFraserDataSet() -#' fds <- calculatePSIValues(fds) -#' fds <- calculateZScores(fds) -calculateZScores <- function(fds, type=psiTypes){ - - # check input - stopifnot(is(fds, "FraserDataSet")) - - # calculate zscore for each psi type - for(pt in type){ - fds <- calculateZScorePerDataSet(fds, pt) - } - - return(fds) -} - -#' -#' calculates the zscore for a given data type and a given psi type -#' and adds it directly to the dataset itself -#' -#' @noRd -calculateZScorePerDataSet <- function(fds, psiType){ - - message(date(), ": Calculate the Zscore for ", psiType, " values ...") - - # get raw data and replace NA's with zeros - psiVal <- assay(fds, psiType) - - # z = ( x - mean ) / sd - rowmean <- rowMeans(psiVal, na.rm = TRUE) - rowsd <- apply(psiVal, 1, sd, na.rm = TRUE) - zscores <- (psiVal - rowmean) / rowsd - - # use as.matrix to rewrite it as a new hdf5 array - zScores(fds, type=psiType) <- as.matrix(zscores) - - return(fds) -} diff --git a/R/countRNAseqData.R b/R/countRNAseqData.R index 366b3551..3212da98 100644 --- a/R/countRNAseqData.R +++ b/R/countRNAseqData.R @@ -475,6 +475,7 @@ countSplitReads <- function(sampleID, fds, NcpuPerSample=1, genome=NULL, } # remove chromosomes with different seqlength than in genome (if provided) + # and remove non annotation existing chromosomes from counting if(!is.null(genome)){ if(is.character(genome)){ genome <- getBSgenome(genome) @@ -483,19 +484,28 @@ countSplitReads <- function(sampleID, fds, NcpuPerSample=1, genome=NULL, chrLengths <- extractChromosomeLengths(bamFile) mismatchChrs <- which(seqlengths(genome)[chromosomes] != chrLengths) if(length(mismatchChrs) > 0){ - warning("Not counting chromosome(s) ", chromosomes[mismatchChrs], + warning("Not counting chromosome(s) ", + paste(chromosomes[mismatchChrs], collapse=", "), " in sample ", sampleID, " as it has a different length", " in the bamFile of this sample than in the provided", " genome.") chromosomes <- chromosomes[-mismatchChrs] } + missingChrs <- which(!chromosomes %in% seqnames(genome)) + if(length(missingChrs) > 0){ + warning("Not counting chromosome(s) ", + paste(chromosomes[missingChrs], collapse=", "), + " in sample ", sampleID, " as it is not specified in", + " the provided genome.") + chromosomes <- chromosomes[-missingChrs] + } } # extract the counts per chromosome countsList <- bplapply(chromosomes, FUN=countSplitReadsPerChromosome, bamFile=bamFile, pairedEnd=pairedEnd, settings=fds, genome=genome, BPPARAM=getBPParam(NcpuPerSample, length(chromosomes))) - + # sort and merge the results befor returning/saving countsGR <- sort(unlist(GRangesList(countsList))) saveRDS(countsGR, cacheFile) @@ -815,9 +825,6 @@ countNonSplicedReads <- function(sampleID, splitCountRanges, fds, # unstranded case: for counting only non spliced reads we # skip this information isPairedEnd <- pairedEnd(fds[,samples(fds) == sampleID])[[1]] - if(isFALSE(as.logical(strandSpecific(fds)))){ - isPairedEnd <- FALSE - } doAutosort <- isPairedEnd # check cache if available diff --git a/R/find_encoding_dimensions.R b/R/find_encoding_dimensions.R index ca75a82d..4b1ad757 100644 --- a/R/find_encoding_dimensions.R +++ b/R/find_encoding_dimensions.R @@ -30,6 +30,9 @@ predict_outliers <- function(fds, type, implementation, BPPARAM){ } eval_prot <- function(fds, type){ + + message(date(), ": Calculating AUC-PR ...") + index <- getSiteIndex(fds, type) idx <- !duplicated(index) @@ -96,19 +99,21 @@ findEncodingDim <- function(i, fds, type, params, implementation, #' @param delayed If FALSE, count matrices will be loaded into memory (faster #' calculations), otherwise the function works on the delayedMatrix #' representations (more memory efficient). The default value depends on the -#' number of samples in the fds-object. +#' number of samples in the fds-object. +#' @param ... Additional parameters passed to \code{injectOutliers}. #' #' @return FraserDataSet #' #' @examples #' # generate data -#' fds <- createTestFraserDataSet() +#' fds <- makeSimulatedFraserDataSet(m=15, j=20) #' #' # run hyperparameter optimization -#' fds <- optimHyperParams(fds, type="psi5", implementation="PCA") +#' fds <- optimHyperParams(fds, type="psi5", q_param=c(2, 5)) #' #' # get estimated optimal dimension of the latent space #' bestQ(fds, type="psi5") +#' hyperParams(fds, type="psi5") #' #' @export optimHyperParams <- function(fds, type, implementation="PCA", @@ -116,7 +121,7 @@ optimHyperParams <- function(fds, type, implementation="PCA", noise_param=0, minDeltaPsi=0.1, iterations=5, setSubset=15000, injectFreq=1e-2, BPPARAM=bpparam(), internalThreads=1, plot=TRUE, - delayed=ifelse(ncol(fds) <= 300, FALSE, TRUE)){ + delayed=ifelse(ncol(fds) <= 300, FALSE, TRUE), ...){ if(isFALSE(needsHyperOpt(implementation))){ message(date(), ": For correction '", implementation, "' no hyper ", "parameter optimization is needed.") @@ -168,8 +173,7 @@ optimHyperParams <- function(fds, type, implementation="PCA", currentType(fds_copy) <- type # inject outliers - fds_copy <- injectOutliers(fds_copy, type=type, freq=injectFreq, - minDpsi=minDeltaPsi, method="samplePSI") + fds_copy <- injectOutliers(fds_copy, type=type, freq=injectFreq, ...) if(sum(getAssayMatrix(fds_copy, type=type, "trueOutliers") != 0) == 0){ warning(paste0("No outliers could be injected so the ", diff --git a/R/fitCorrectionMethods.R b/R/fitCorrectionMethods.R index 79bcc659..9c573791 100644 --- a/R/fitCorrectionMethods.R +++ b/R/fitCorrectionMethods.R @@ -22,7 +22,7 @@ fit <- function(fds, implementation=c("PCA", "PCA-BB-Decoder", "AE", counts(fds, type=type, side="ofInterest")) # check q is set - if(method != "BB" & (missing(q) | is.null(q))){ + if(method != "BB" && (missing(q) | is.null(q))){ stop("Please provide a q to define the size of the latent space!") } @@ -168,7 +168,7 @@ fit <- function(fds, implementation=c("PCA", "PCA-BB-Decoder", "AE", nSubset = nSubset, minDeltaPsi = minDeltaPsi ), - BB = fitBB(fds = fds, psiType = type) + BB = fitBB(fds=fds, psiType=type, BPPARAM=BPPARAM) ) return(fds) @@ -208,7 +208,7 @@ getHyperOptimCorrectionMethod <- function(correction){ } fitPCA <- function(fds, q, psiType, rhoRange=c(1e-5, 1-1e-5), noiseAlpha=NULL, - BPPARAM=bpparam(), subset=FALSE, minDeltaPsi, + BPPARAM=bpparam(), subset=FALSE, minDeltaPsi=0.1, nSubset=15000, useLM=FALSE){ counts(fds, type=psiType, side="other", HDF5=FALSE) <- as.matrix( counts(fds, type=psiType, side="other")) @@ -279,15 +279,14 @@ fitPCA <- function(fds, q, psiType, rhoRange=c(1e-5, 1-1e-5), noiseAlpha=NULL, return(fds) } -fitBB <- function(fds, psiType){ +fitBB <- function(fds, psiType, BPPARAM=bpparam()){ currentType(fds) <- psiType fds <- pvalueByBetaBinomialPerType(fds=fds, - aname=paste0("pvalues_BB_", psiType), - psiType=psiType, - pvalFun=betabinVglmTest) + aname=paste0("pvalues_BB_", psiType), + psiType=psiType, pvalFun=betabinVglmTest, BPPARAM=BPPARAM) # predictedMeans(fds, type=psiType) <- rowMeans( # getAssayMatrix(fds, type=psiType)) - predictedMeans(fds, type=psiType) <- + predictedMeans(fds, type=psiType, withDimnames=FALSE) <- mcols(fds, type=psiType)[,paste0(psiType, "_alpha")] / ( mcols(fds, type=psiType)[,paste0(psiType, "_alpha")] + mcols(fds, type=psiType)[,paste0(psiType, "_beta")] ) diff --git a/R/getNSetterFuns.R b/R/getNSetterFuns.R index ee89607c..7c0beea0 100644 --- a/R/getNSetterFuns.R +++ b/R/getNSetterFuns.R @@ -280,11 +280,7 @@ pVals <- function(fds, type=currentType(fds), level="site", #' @export padjVals <- function(fds, type=currentType(fds), dist=c("BetaBinomial"), ...){ dist <- match.arg(dist, choices=c("BetaBinomial", "Binomial", "Normal")) - if( paste(paste0("padj", dist), type, sep="_") %in% assayNames(fds)){ - return(getAssayMatrix(fds, paste0("padj", dist), type=type, ...)) - } else{ - return(getAssayMatrix(fds, paste0("pajd", dist), type=type, ...)) - } + return(getAssayMatrix(fds, paste0("padj", dist), type=type, ...)) } `padjVals<-` <- function(fds, type=currentType(fds), @@ -422,7 +418,11 @@ bestNoise <- function(fds, type=currentType(fds)){ #' assays should be stored as hdf5 files. #' @export dontWriteHDF5 <- function(fds){ - return(metadata(fds)[['dontWriteHDF5']]) + ans <- metadata(fds)[['dontWriteHDF5']] + if(is.null(ans)){ + ans <- FALSE + } + return(ans) } #' @describeIn getter_setter_functions Sets whether the assays should be stored diff --git a/R/makeSimulatedDataset.R b/R/makeSimulatedDataset.R index dad3caf4..a27dabdc 100644 --- a/R/makeSimulatedDataset.R +++ b/R/makeSimulatedDataset.R @@ -25,21 +25,21 @@ #' #' @rdname makeSimulatedFraserDataSet #' @aliases makeSimulatedFraserDataSet -#' @noRd +#' @export makeSimulatedFraserDataSet <- function(m=100, j=500, q=10, - distribution=c("BB", "DM"), ...){ + distribution=c("BB", "DM"), ...){ distribution <- match.arg(distribution) fds <- switch (distribution, BB = makeSimulatedFraserDataSet_BetaBinomial(m=m, j=j, q=q, ...), DM = makeSimulatedFraserDataSet_Multinomial(m=m, j=j, q=q, ...)) - + fds } makeSimulatedFraserDataSet_BetaBinomial <- function(m=200, j=10000, q=10, - nbMeanLog=8, nbSize=0.55, rhoMeanlog=-5, rhoSdlog=1, - Hmean=0, Hsd=100, Dmean=0, Dsd=0.007, ...){ - + nbMeanLog=8, nbSize=0.55, rhoMeanlog=-5, rhoSdlog=1, + Hmean=0, Hsd=100, Dmean=0, Dsd=0.007, ...){ + # Simulate total coverage N = nonSplit + n at each junction using lognormal junction_n <- rlnorm(j, meanlog=nbMeanLog, sdlog=nbSize) N <- t(vapply(junction_n, function(x){ @@ -48,124 +48,124 @@ makeSimulatedFraserDataSet_BetaBinomial <- function(m=200, j=10000, q=10, double(m))) # N <- matrix(rnbinom(j*m, mu=nbMean, size=nbSize), nrow=j, ncol=m) mode(N) <- 'integer' - + # # Simulate covariates for SE # H_true <- matrix(rnorm(m*q, mean=Hmean, sd=Hsd), nrow=m, ncol=q) D_true <- matrix(rnorm(j*q, mean=Dmean, sd=Dsd), nrow=j, ncol=q) b_true <- sample(c(rnorm(round(j*(1/6)), mean=3.5, sd=1.5), - rnorm(round(j*(5/6)), mean=-2.5, sd=2.5))) + rnorm(j - round(j*(1/6)), mean=-2.5, sd=2.5))) y_se <- t(predictYCpp(H_true, D_true, b_true)) mu_se <- predictMuCpp(y_se) # betaBin dispersion rho_se <- rlnorm(j, meanlog=rhoMeanlog, sdlog=rhoSdlog) - + # Draw nonSplit reads from BB nonSplit <-matrix(rbetabinom(j*m, size=N, prob=mu_se, rho=rho_se), nrow=j, ncol=m) mode(nonSplit) <- 'integer' - + # Set n = N - nonSplit n <- N - nonSplit - + # # Simulate covariates for PSI3=PSI5 # H_true <- matrix(rnorm(m*q, mean=Hmean, sd=Hsd), nrow=m, ncol=q) D_true <- matrix(rnorm(j*q, mean=Dmean, sd=Dsd), nrow=j, ncol=q) b_true <- sample(c(rnorm(round(j*(1/6)), mean=-2.5, sd=2.5), - rnorm(round(j*(5/6)), mean=3.5, sd=1.5))) + rnorm(j-round(j*(1/6)), mean=3.5, sd=1.5))) y_psi <- t(predictYCpp(H_true, D_true, b_true)) - mu_psi <- predictMuCpp(y_psi) + mu_psi <- predictMuCpp(y_psi) # betaBin dispersion rho_psi <- rlnorm(j, meanlog=rhoMeanlog, sdlog=rhoSdlog) - + # Draw nonSplit reads from BB - k <-matrix(rbetabinom(j*m, size=n, prob=mu_psi, rho=rho_psi), + k <- matrix(rbetabinom(j*m, size=n, prob=mu_psi, rho=rho_psi), nrow=j, ncol=m) mode(k) <- 'integer' - - + + # # Create FRASER data set # sampleIDs <- paste0("sample", seq_len(m)) anno <- data.table(sampleID = sampleIDs, bamFile=rep(NA, m)) fds <- FraserDataSet(colData=anno, ...) - + # put in n as rawcountsJ first so it doesn't complain later # when assinging k to it junctionData <- SummarizedExperiment( colData=colData(fds), assays=list(rawCountsJ=k), rowRanges=GRanges(seqnames=rep("chr1", j), - ranges=IRanges(start=seq_len(j), width=1 )) - ) + ranges=IRanges(start=seq_len(j), width=1 ))) + nonSplitData <- SummarizedExperiment( colData=colData(fds), assays=list(rawCountsSS=nonSplit), rowRanges=GRanges(seqnames=rep("chr1", j), - ranges=IRanges(start=seq_len(j), width=1 )) - ) + ranges=IRanges(start=seq_len(j), width=1 ))) + fds <- new("FraserDataSet", junctionData, name = name(fds), bamParam = scanBamParam(fds), strandSpecific = strandSpecific(fds), workingDir = workingDir(fds), - nonSplicedReads = nonSplitData - ) + nonSplicedReads = nonSplitData) + metadata(fds)[['optimalEncDim']] <- q metadata(fds)[['encDimTable']] <- data.table( encodingDimension=q, evaluationLoss=1, evalMethod='simulation') - + # Store "other" counts counts(fds, type="psi3", side="other", withDimnames=FALSE) <- n - k counts(fds, type="psi5", side="other", withDimnames=FALSE) <- n - k counts(fds, type="psiSite", side="other", withDimnames=FALSE) <- n - + # store information about the simulation in the fds # (same values for psi3 and psi5) for(type in c("psi3", "psi5")){ setAssayMatrix(fds=fds, name="truePSI", type=type, - withDimnames=FALSE) <- mu_psi + withDimnames=FALSE) <- mu_psi setAssayMatrix(fds=fds, name="trueLogitPSI", type=type, - withDimnames=FALSE) <- y_psi - mcols(fds, type=type)[,"trueRho"] <- rho_psi - + withDimnames=FALSE) <- y_psi + mcols(fds, type=type)[,"trueRho"] <- rho_psi + # needed so that subsetting the fds works later mcols(fds, type=type)[["startID"]] <- seq_len(nrow(mcols(fds, type=type))) mcols(fds, type=type)[["endID"]] <- seq_len(nrow(mcols(fds, type=type))) } - + # store info for SE setAssayMatrix(fds=fds, name="truePSI", type="psiSite", - withDimnames=FALSE) <- mu_se + withDimnames=FALSE) <- mu_se setAssayMatrix(fds=fds, name="trueLogitPSI", type="psiSite", - withDimnames=FALSE) <- y_se + withDimnames=FALSE) <- y_se mcols(fds, type="psiSite")[,"trueRho"] <- rho_se - + # needed so that subsetting the fds works later siteIDs <- seq_row(mcols(fds, type="psiSite")) mcols(fds, type="psiSite")[["startID"]] <- siteIDs mcols(fds, type="psiSite")[["endID"]] <- siteIDs mcols(fds, type="psiSite")[["spliceSiteID"]] <- siteIDs - + return(fds) - + } # # Generate a simulated fds using a Dirichlet-Mulitnomial distribution # makeSimulatedFraserDataSet_Multinomial <- function(m=200, j=1000, q=10, - groups=round(j*0.65), nbMeanLog=8, nbSize=0.55, - rhoMeanlog=-5, rhoSdlog=1, Hmean=0, Hsd=100, Dmean=0, - Dsd=0.007, ...){ - + groups=round(j*0.65), nbMeanLog=8, nbSize=0.55, + rhoMeanlog=-5, rhoSdlog=1, Hmean=0, Hsd=100, Dmean=0, + Dsd=0.007, ...){ + # # Simulate groups of junctions having the same donor/acceptor site # @@ -173,12 +173,12 @@ makeSimulatedFraserDataSet_Multinomial <- function(m=200, j=1000, q=10, donorGroups <- seq_len(d) # ids of the groups (1,2,...,d) # assign junction to donor/acceptor groups junctionGroups <- sort(sample(x = donorGroups, size=j, replace = TRUE)) - + # Set d to the actual number of groups (some might not have been assigned # any junction at all) donorGroups <- donorGroups[donorGroups %in% unique(junctionGroups)] d <- length(donorGroups) - + # # SE simulated as additional junction for every group # @@ -186,8 +186,8 @@ makeSimulatedFraserDataSet_Multinomial <- function(m=200, j=1000, q=10, psiJunctionGroups <- junctionGroups # store for later j <- j + d # add one junction for every group junctionGroups <- sort(c(junctionGroups, donorGroups)) - - + + # # Simulate n for each junction using lognormal and negative binomial # @@ -197,7 +197,7 @@ makeSimulatedFraserDataSet_Multinomial <- function(m=200, j=1000, q=10, n <- rnbinom(m, mu=x, size=nbSize)}, double(m))) # n <- matrix(rnbinom(j*m, mu=nMean, size=theta), nrow=j, ncol=m) - + # Sum up the values of n within one donor/acceptor group to get the n value # of the group dt_n <- as.data.table(n) @@ -205,16 +205,16 @@ makeSimulatedFraserDataSet_Multinomial <- function(m=200, j=1000, q=10, res <- lapply(colnames(dt_n),function(x){ sum_n[,c(paste0(x, "_sum")):=sum(get(x)),by=junctionGroups] }) - + # Extract final n matrix (all junctions within a group have the same n # value) sum_cols <- paste0(colnames(dt_n), "_sum") n <- as.matrix(sum_n[,..sum_cols]) colnames(n) <- NULL mode(n) <- 'integer' - - - + + + # # Simulate betaBin dispersion for every donor/acceptor group, same # dispersion for all junctions within a group @@ -225,9 +225,9 @@ makeSimulatedFraserDataSet_Multinomial <- function(m=200, j=1000, q=10, dt2 <- data.table(group=junctionGroups) rhoFull <- merge(dt1, dt2, by="group") rho <- rhoFull[,rho] # set dispersion for every junction - - - + + + # # Simulate covariates. # @@ -236,7 +236,7 @@ makeSimulatedFraserDataSet_Multinomial <- function(m=200, j=1000, q=10, b_true <- double(j) y_true <- t(predictYCpp(H_true, D_true, b_true)) ae_mu <- predictMuCpp(y_true) - + # Use softmax on mu to get mu within one group to sum up to 1 softmax <- function(x){ sum <- sum(exp(x)) @@ -247,170 +247,170 @@ makeSimulatedFraserDataSet_Multinomial <- function(m=200, j=1000, q=10, return( apply(group_mu, 2, softmax) ) }) mu <- do.call(rbind, softmax_mu) - + # Calcluate alpha values alpha <- mu * (1-rho)/rho - - - + + + # # Simulate psi3=psi5 and SE jointly with Dirchilet-Multinomial # res <- vapply(donorGroups, function(groupID){ - + # Get the indices of the junctions in this group pos <- which(junctionGroups == groupID) - + # get alpha and n values for this group group_alpha <- alpha[pos,] group_n <- n[pos,] - + # draw counts from a dirichlet multinomial distribution # (jointly for PSi and SE) counts <- t(rdirmnom(m, group_n[1,], t(group_alpha))) - + # First row of resulting counts represents non split reads for SE, # the other rows are the counts for the "real" junctions nonSplit <- counts[1,] k <- counts[-1,] - + # Substract nonSplit reads from total N to get n for PSI (=other for SE) group_n <- group_n - matrix(nonSplit, nrow=nrow(group_n), - ncol=ncol(group_n), byrow = TRUE) - + ncol=ncol(group_n), byrow = TRUE) + # Also get mu and rho for this group (to split it into PSI and SE part # for storing later) group_mu <- mu[pos,] group_rho <- rho[pos] - + # First row/value is the one for SE se_n <- group_n[1,] se_mu <- group_mu[1,] se_alpha <- group_alpha[1,] se_rho <- group_rho[1] - + # The other rows/values are the ones relevant for PSI psi_n <- group_n[-1,] psi_alpha <- group_alpha[-1,] psi_rho <- group_rho[-1] psi_mu <- group_mu[-1,] if(is.null(nrow(psi_mu))){ # only one junction in the group, convert - # to matrix with one row so that colSums - # works + # to matrix with one row so that colSums + # works psi_mu <- matrix(psi_mu, nrow=1) } # scale mu's so that they sum up to 1 again (after mu for SE is removed) psi_mu <- psi_mu / matrix(colSums(psi_mu), nrow=nrow(psi_mu), - ncol=ncol(psi_mu), byrow = TRUE) - + ncol=ncol(psi_mu), byrow = TRUE) + # return everyting relevant for storing counts, n, alpha, mu, # rho split into the parts for PSI and SE return(list(k=k, nonSplit=nonSplit, n=psi_n, group_n=se_n, - psi_mu=psi_mu, se_mu=se_mu, psi_alpha=psi_alpha, - se_alpha=se_alpha, psi_rho=psi_rho, se_rho=se_rho)) + psi_mu=psi_mu, se_mu=se_mu, psi_alpha=psi_alpha, + se_alpha=se_alpha, psi_rho=psi_rho, se_rho=se_rho)) }, FUN.VALUE=list(k=numeric(m), nonSplit=numeric(m), n=numeric(m), - group_n=numeric(m), psi_mu=numeric(m), - se_mu=numeric(m), psi_alpha=numeric(m), - se_alpha=numeric(m), psi_rho=numeric(1), - se_rho=numeric(1))) - + group_n=numeric(m), psi_mu=numeric(m), + se_mu=numeric(m), psi_alpha=numeric(m), + se_alpha=numeric(m), psi_rho=numeric(1), + se_rho=numeric(1))) + # Extract k and nonSplit reads k <- do.call(rbind, res[1,]) nonSplit <- do.call(rbind, res[2,]) mode(k) <- 'integer' mode(nonSplit) <- 'integer' - + # Extract n value for each junction and group n value for SE psi_n <- do.call(rbind, res[3,]) se_n <- do.call(rbind, res[4,]) - + # Extract mu values for PSI and SE psi_mu <- do.call(rbind, res[5,]) se_mu <- do.call(rbind, res[6,]) - + # Extract alpha values for PSI and SE psi_alpha <- do.call(rbind, res[7,]) se_alpha <- do.call(rbind, res[8,]) - + # Extract rho values for PSI and SE psi_rho <- unlist(res[9,]) se_rho <- unlist(res[10,]) - - - + + + # # Create FRASER data set # sampleIDs <- paste0("sample", seq_len(m)) anno <- data.table(sampleID = sampleIDs, bamFile=rep(NA, m)) fds <- FraserDataSet(colData=anno, ...) - + # put in k as rawcountsJ (= nr reads spanning each junction) junctionData <- SummarizedExperiment( colData=colData(fds), assays=list(rawCountsJ=k), rowRanges=GRanges(seqnames=rep("chr1", nrJunctions), - ranges=IRanges(start=psiJunctionGroups, width=1)) - ) + ranges=IRanges(start=psiJunctionGroups, width=1))) + nonSplitData <- SummarizedExperiment( colData=colData(fds), assays=list(rawCountsSS=nonSplit), rowRanges=GRanges(seqnames=rep("chr1", d), - ranges=IRanges(start=donorGroups, width=1)) - ) + ranges=IRanges(start=donorGroups, width=1))) + fds <- new("FraserDataSet", - junctionData, - name = name(fds), - bamParam = scanBamParam(fds), - strandSpecific = strandSpecific(fds), - workingDir = workingDir(fds), - nonSplicedReads = nonSplitData - ) + junctionData, + name = name(fds), + bamParam = scanBamParam(fds), + strandSpecific = strandSpecific(fds), + workingDir = workingDir(fds), + nonSplicedReads = nonSplitData) + metadata(fds)[['optimalEncDim']] <- q - - + + # store other counts (n-k) for psi3=psi5 and SE (other=n) counts(fds, type="psi3", side="other", withDimnames=FALSE) <- psi_n - k counts(fds, type="psi5", side="other", withDimnames=FALSE) <- psi_n - k counts(fds, type="psiSite", side="other", withDimnames=FALSE) <- se_n - + for(type in c("psi3", "psi5")){ # store information about the simulation in the fds # (same values for psi3 and psi5) setAssayMatrix(fds=fds, name="truePSI", type=type, - withDimnames=FALSE) <- psi_mu + withDimnames=FALSE) <- psi_mu setAssayMatrix(fds=fds, name="trueLogitPSI", type=type, - withDimnames=FALSE) <- qlogis(psi_mu) + withDimnames=FALSE) <- qlogis(psi_mu) setAssayMatrix(fds=fds, name="trueAlpha", type=type, - withDimnames=FALSE) <- psi_alpha - mcols(fds, type=type)[,"trueRho"] <- psi_rho - + withDimnames=FALSE) <- psi_alpha + mcols(fds, type=type)[,"trueRho"] <- psi_rho + # needed so that subsetting the fds works later mcols(fds, type=type)[["startID"]] <- - psiJunctionGroups # 1:nrow(mcols(fds, type=type)) + psiJunctionGroups # 1:nrow(mcols(fds, type=type)) mcols(fds, type=type)[["endID"]] <- - psiJunctionGroups # 1:nrow(mcols(fds, type=type)) + psiJunctionGroups # 1:nrow(mcols(fds, type=type)) } - + # store info for SE setAssayMatrix(fds=fds, name="truePSI", type="psiSite", - withDimnames=FALSE) <- se_mu + withDimnames=FALSE) <- se_mu setAssayMatrix(fds=fds, name="trueLogitPSI", type="psiSite", - withDimnames=FALSE) <- qlogis(se_mu) + withDimnames=FALSE) <- qlogis(se_mu) setAssayMatrix(fds=fds, name="trueAlpha", type="psiSite", - withDimnames=FALSE) <- se_alpha + withDimnames=FALSE) <- se_alpha mcols(fds, type="psiSite")[,"trueRho"] <- se_rho - + # needed so that subsetting the fds works later mcols(fds, type="psiSite")[["startID"]] <- - donorGroups # 1:nrow(mcols(fds, type="psiSite")) + donorGroups # 1:nrow(mcols(fds, type="psiSite")) mcols(fds, type="psiSite")[["endID"]] <- - seq_len(nrow(mcols(fds, type="psiSite"))) + seq_len(nrow(mcols(fds, type="psiSite"))) mcols(fds, type="psiSite")[["spliceSiteID"]] <- - donorGroups # 1:nrow(mcols(fds, type="psiSite")) - + donorGroups # 1:nrow(mcols(fds, type="psiSite")) + return(fds) - + } #' @@ -430,18 +430,20 @@ makeSimulatedFraserDataSet_Multinomial <- function(m=200, j=1000, q=10, #' adding to the mean psi of the junction over all samples or "samplePSI" to #' add to the psi value of the junction in the specific sample. "simulatedPSI" #' is only possible if a simulated dataset is used. +#' @param BPPARAM A BiocParallel object to run the computation in parallel #' #' @return FraserDataSet #' #' @examples -#' # An example dataset -#' fds <- createTestFraserDataSet() +#' # A generic dataset +#' fds <- makeSimulatedFraserDataSet() #' fds <- injectOutliers(fds, minDpsi=0.2, freq=1E-3) #' @export injectOutliers <- function(fds, type=c("psi5", "psi3", "psiSite"), freq=1E-3, minDpsi=0.2, minCoverage=2, deltaDistr="uniformDistr", verbose=FALSE, - method=c('meanPSI', 'samplePSI', 'simulatedPSI')){ + method=c('samplePSI', 'meanPSI', 'simulatedPSI'), + BPPARAM=bpparam()){ type <- match.arg(type, several.ok=TRUE) method <- match.arg(method) @@ -449,278 +451,297 @@ injectOutliers <- function(fds, type=c("psi5", "psi3", "psiSite"), for(t in type){ fds <- injectOutliers(fds, type=t, freq=freq, minDpsi=minDpsi, minCoverage=minCoverage, deltaDistr=deltaDistr, - verbose=verbose, method=method) + verbose=verbose, method=method, BPPARAM=BPPARAM) } return(fds) } - - # get infos from the fds - if(paste0("originalCounts_", type) %in% assayNames(fds)){ - message("Use existing original counts and reinject outliers.") - k <- as.matrix(getAssayMatrix(fds, type=type, "originalCounts")) - o <- as.matrix(getAssayMatrix(fds, type=type, "originalOtherCounts")) - } else { - k <- as.matrix(counts(fds, type=type, side="ofI")) - o <- as.matrix(counts(fds, type=type, side="other")) + # copy original k and o + if(type == "psiSite"){ + setAssayMatrix(fds, type="psiSite", "originalCounts", + withDimnames=FALSE) <- + counts(fds, type="psiSite", side="ofInterest") + setAssayMatrix(fds, type="psiSite", "originalOtherCounts", + withDimnames=FALSE) <- + counts(fds, type="psiSite", side="other") } - n <- k + o - m <- ncol(k) - j <- nrow(k) - - # compute psi - psi <- switch(method, + else{ + setAssayMatrix(fds, type=type, "originalCounts", + withDimnames=FALSE) <- + counts(fds, type=type, side="ofInterest") + setAssayMatrix(fds, type="psi5", "originalOtherCounts", + withDimnames=FALSE) <- + counts(fds, type="psi5", side="other") + setAssayMatrix(fds, type="psi3", "originalOtherCounts", + withDimnames=FALSE) <- + counts(fds, type="psi3", side="other") + } + + # get infos from the fds + m <- ncol(fds) + j <- nrow(mcols(fds, type=type)) + + k <- as.matrix(K(fds, type=type)) + n <- as.matrix(N(fds, type=type)) + o <- as.matrix(counts(fds, type=type, side="other")) + + psi <- switch(match.arg(method), samplePSI = (k + pseudocount())/(n + 2*pseudocount()), meanPSI = matrix(nrow=j, ncol=m, - rowMeans( (k + pseudocount())/(n + 2*pseudocount()) )), - simulatedPSI = getAssayMatrix(fds, "truePSI", type=type)) - - # - # matrix of donor/acceptor for possible injection - # - tmpIndex <- getSiteIndex(fds, type) - dt <- as.data.table(rowRanges(fds, type=type))[,.( - chr=seqnames, start, end, strand, idxInCount=.I, - junctionID=tmpIndex)] - dt[,nPerGroup:=.N,by=junctionID] - dt[,idxGroup:=.GRP, by=junctionID] - dt[,idxInGroup:=seq_len(.N),by=junctionID] - - if(max(dt$idxGroup) * m * freq < 10){ - freq <- 10/(max(dt$idxGroup) * m) - warning("Injection-frequency is to low. Increasing it to `", - signif(freq, 2), "` so we can inject at least 10 events ", - "into the data set!") - } - - # where do we inject and how - indexOut_groups <- matrix(ncol=m, sample(c(0,1,-1), - max(dt$idxGroup)*m, replace=TRUE, - prob=c(1-freq, freq/2, freq/2))) - - # positions where outliers will be injected - list_index <- which(indexOut_groups != 0, arr.ind=TRUE) - - - # sample primary injection - primary <- NULL - primaryInjection <- merge(as.data.table(list_index), dt, - sort=FALSE, by.x="row", by.y="idxGroup", allow.cartesian=TRUE) - primaryInjection[,primary:=idxInGroup==sample(idxInGroup,1),by="row,col"] - primaryInjection[,n:=n[cbind(idxInCount, col)]] - primaryInjection[,k:=k[cbind(idxInCount, col)]] - primaryInjection[,nOk:=length(unique(n)) == 1, by="row,col"] - if(nrow(primaryInjection[nOk == FALSE]) / nrow(primaryInjection) > 0.05){ - # this can happen in the lower range if the prediction of the direction - # did not work properly. Where the donor/acceptor goes the wrong way. - # But this should not be too frequent. So ignore if it is below 5% - warning("It looks like you have a problem with your indexes. ", - "Please report it on github to the developer.") + rowMeans( (k + pseudocount()) / (n + 2*pseudocount()))), + simulatedPSI = getAssayMatrix(fds, "truePSI", type=type) ) + + # nedded to have comparably many outliers when type is SE + if(type == "psiSite"){ + freq <- freq/10 } - primaryInjection <- primaryInjection[nOk == TRUE] - primaryInjection[,I:=cumsum(primary)] - primaryInjection[,I:=max(I),by="row,col"] - - - # get matrix accessors (row, col) aka (junctionID, sampleID) - primaryIndexInReal <- as.matrix( - primaryInjection[primary == TRUE, .(idxInCount, col)]) - primaryIndexInGroup <- as.matrix( - primaryInjection[primary == TRUE, .(row, col)]) - secondaryIndexInReal <- as.matrix( - primaryInjection[primary != TRUE, .(idxInCount, col)]) - secondaryIndexInGroup <- as.matrix( - primaryInjection[primary != TRUE, .(row, col)]) - # init with empty for psiSite. Function rep does not - # support times of length zero eg: rep(1, integer(0)) - secondaryPrimaryIndex <- as.matrix(primaryInjection[FALSE,.( - row, col, idxInCount, junctionID, nPerGroup, I, rep=integer(.N))]) - if(nrow(primaryInjection[primary == TRUE & nPerGroup > 1]) > 0){ - secondaryPrimaryIndex <- as.matrix(primaryInjection[ - primary == TRUE & nPerGroup > 1, .( - idxInCount, junctionID, nPerGroup, I, - rep=rep(TRUE, nPerGroup-1)), - by="row,col"]) + + # data table with information about chr, start, end, + # ... for calculating groups + dt <- if(type == "psiSite"){ + data.table( + junctionID = 1:j, + chr = as.factor(seqnames(nonSplicedReads(fds))), + start = start(nonSplicedReads(fds)), + end = end(nonSplicedReads(fds)), + strand = as.factor(strand(nonSplicedReads(fds))) ) + }else{ + data.table( + junctionID = 1:j, + chr = as.factor(seqnames(fds)), + start = start(fds), + end = end(fds), + strand = as.factor(strand(fds)) ) } + dt[, donorGroupSize:=length(junctionID), by=c("chr", "start", "strand")] + dt[, acceptorGroupSize:=length(junctionID), by=c("chr", "end", "strand")] + dt[, donorGroupID := .GRP, by=c("chr", "start", "strand")] + dt[, acceptorGroupID:=.GRP, by=c("chr", "end", "strand")] + + # Get groups where outlier can be injected + available_groups <- switch(type, + psi3 = dt[acceptorGroupSize > 1, acceptorGroupID, + by=acceptorGroupID]$acceptorGroupID, + psi5 = dt[donorGroupSize > 1, donorGroupID, + by=donorGroupID]$donorGroupID, + psiSite = seq_len(j)) - # get direction of injection - injDirection <- indexOut_groups[primaryIndexInGroup] - # table(injDirection) - - # get current psi of this junction and calculate maximal possible delta psi - junction_psi <- psi[primaryIndexInReal] - maxDpsi <- ifelse(injDirection > 0, 1 - junction_psi, junction_psi) - - # if not possible to inject here with at least minDpsi, - # change injection direction - injectionPossible <- maxDpsi > minDpsi - # table(injectionPossible) - injDirection[!injectionPossible] <- -injDirection[!injectionPossible] - maxDpsi <- ifelse(injDirection > 0, 1 - junction_psi, junction_psi) - - # ensure that injected points becomes an outlier by adding - # mean delta psi as an offset - meanDpsi <- rowMeans2(abs(psi[primaryIndexInReal[,"idxInCount"],] - - rowMeans2(psi[primaryIndexInReal[,"idxInCount"],]))) - - # sample delta psi for injection from uniform distribution between - # min and max dpsi - injMinDpsi <- ifelse(minDpsi + meanDpsi < maxDpsi, - minDpsi + meanDpsi, maxDpsi) - injDpsi <- injDirection * switch(deltaDistr, - uniformDistr = runif(length(injMinDpsi), injMinDpsi, maxDpsi), - ifelse(deltaDistr > maxDpsi, maxDpsi, deltaDistr)) - - # get N of this junction - n_ji <- n[primaryIndexInReal] - - # inject new primary k_ij -> change k based on n and new_psi - # (and take pseudocounts into account): (k+1)/(n+2)=psi -> k = psi*(n+2)-1 - new_primary_psi <- junction_psi + injDpsi - new_primary_k <- pmax(pmin(round( - new_primary_psi*(n_ji + 2*pseudocount()) - pseudocount()), n_ji), 0) - - # inject secondary k_ij -> change k based on n and the new_psi - second_delta_psi <- - injDpsi[secondaryPrimaryIndex[,"I"]] * ( - psi[secondaryIndexInReal] / ( - 1-psi[secondaryPrimaryIndex[,c("idxInCount", "col"),drop=FALSE]])) - new_second_psi <- psi[secondaryIndexInReal] + second_delta_psi - - # sanity check for injected psi - if(any(new_second_psi < -0.0001) | any(new_second_psi > 1.0001)){ - warning("Have to cut injected delta psi for: ", - sum(new_second_psi < -0.0001), " and ", - sum(new_second_psi > 1.0001), " instances!") + # e.g. for psi3/5: no donor/acceptor + # groups with at least 2 junctions (e.g in simulationBB) + if(length(available_groups) == 0){ + available_groups <- seq_len(j) + freq <- freq/10 } - new_second_psi <- pmin(pmax(new_second_psi, 0), 1) - new_second_k <- pmax(pmin(round(new_second_psi * (( - n[secondaryIndexInReal] + 2 * pseudocount()) - - pseudocount())), - n[secondaryIndexInReal]), 0) - - # - # check if we really have an outlier or not - # and get primiary/secondary injections - # - goodInjections <- n[primaryIndexInReal] >= minCoverage & - abs(k[primaryIndexInReal] - new_primary_k) > 2 - goodJunctionIds <- primaryInjection[primary == TRUE][ - goodInjections,junctionID] - goodSecondary <- primaryInjection[primary == FALSE][, - junctionID %in% goodJunctionIds] - - message(paste0(date(), ": Injecting outliers: ", sum(goodInjections), - " / ", sum(goodSecondary), " (primary/secondary)")) - - # - # prepare the injection into the dataset only for those which passed QC - # - - # set counts (k and o) - k[primaryIndexInReal[goodInjections,,drop=FALSE]] <- - new_primary_k[goodInjections] - k[secondaryIndexInReal[goodSecondary,,drop=FALSE]] <- - new_second_k[goodSecondary] - o <- n - k - - # set injection status (direction, primary, secondary) - indexOut <- matrix(0, nrow=nrow(k), ncol=ncol(k)) - indexOut[primaryIndexInReal[goodInjections,]] <- - injDirection[goodInjections] - indexOut[secondaryIndexInReal[goodSecondary,]] <- - 2 * injDirection[secondaryPrimaryIndex[,"I"][goodSecondary]] - - # set injected delta psi - indexDeltaPsi <- matrix(0, nrow=nrow(k), ncol=ncol(k)) - indexDeltaPsi[primaryIndexInReal[goodInjections,]] <- - injDpsi[goodInjections] - indexDeltaPsi[secondaryIndexInReal[goodSecondary,]] <- - second_delta_psi[goodSecondary] - - # - # do the injection and save the additional informations in the object - # - # copy original k and o - if(type == "psiSite"){ - setAssayMatrix(fds, type="psiSite", "originalCounts", - withDimnames=FALSE) <- - counts(fds, type="psiSite", side="ofInterest") - setAssayMatrix(fds, type="psiSite", "originalOtherCounts", - withDimnames=FALSE) <- - counts(fds, type="psiSite", side="other") - } else { - setAssayMatrix(fds, type=type, "originalCounts", - withDimnames=FALSE) <- - counts(fds, type=type, side="ofInterest") - setAssayMatrix(fds, type="psi5", "originalOtherCounts", - withDimnames=FALSE) <- - counts(fds, type="psi5", side="other") - setAssayMatrix(fds, type="psi3", "originalOtherCounts", - withDimnames=FALSE) <- - counts(fds, type="psi3", side="other") + list_index <- data.frame(row=integer(0), col=integer(0)) + count <- 0 + while(nrow(list_index) < 1){ + if(count == 20){ + stop("Could not inject at least 2 outliers.", + " Please make sure you have enough junctions and samples", + " in you dataset.") + } + count <- count + 1 + + indexOut_groups <- matrix(sample(c(0,1,-1), length(available_groups)*m, + replace=TRUE, prob=c(1-freq, freq/2, freq/2)), ncol=m) + + # positions where outliers will be injected + list_index <- which(indexOut_groups != 0, arr.ind = TRUE) } - # store new k and o counts including the outlier counts - counts(fds, type=type, side="ofInterest", withDimnames=FALSE) <- k - counts(fds, type=type, side="other", withDimnames=FALSE) <- o + # apply injection function to each outlier + message(date(), ": Injecting ", nrow(list_index), " outliers ...") + result <- bplapply(seq_len(nrow(list_index)), list_index=list_index, + indexOut_groups=indexOut_groups, type=type, psi=psi, n=n, dt=dt, + minDpsi=minDpsi, verbose=verbose, BPPARAM=SerialParam(), + FUN=function(j, list_index, indexOut_groups, type, + psi, n, dt=dt, minDpsi, verbose){ + # extract group, sample and injecetion + # direction (i.e +1/up or -1/down) + row <- list_index[j,'row'] + group <- available_groups[row] + sample <- list_index[j,'col'] + injDirection <- indexOut_groups[row, sample] + + # sample one junction from all junction within this group + group_junctions <- if(type == "psi3"){ + dt[acceptorGroupID == group, junctionID] } + else { + dt[donorGroupID == group, junctionID] } + junction <- if(length(group_junctions)==1){ + group } + else { + sample(group_junctions, 1) } + + # get current psi of this junction and calculate + # maximla possible value of delta psi for the injection + junction_psi <- psi[junction, sample] + maxDpsi <- if(injDirection > 0){ + 1 - junction_psi } + else{ + junction_psi } + + # if not possible to inject here with at least minDpsi, + # change injection direction + if(maxDpsi < minDpsi){ + injDirection <- -injDirection + indexOut_groups[row, sample] <- injDirection + maxDpsi <- if(injDirection > 0){ + 1 - junction_psi } + else{ + junction_psi } + } + + # sample delta psi for injection from uniform + # distribution between min and max dpsi + minDpsi <- ifelse(minDpsi < maxDpsi, minDpsi, maxDpsi) + injDpsi <- injDirection * switch(as.character(deltaDistr), + uniformDistr = runif(1, minDpsi, maxDpsi), + ifelse(as.double(deltaDistr) > maxDpsi, + maxDpsi, as.double(deltaDistr)) ) + + # get N of this junction + n_ji <- n[junction,sample] + + # new counts after injection + newKs <- integer(length(group_junctions)) + indexDpsi <- double(length(group_junctions)) + indOut <- integer(length(group_junctions)) + + # for all other junctions in this group + for(i in seq_len(length(group_junctions))){ + junction_k <- group_junctions[i] + # get new_psi and change k based on n and new_psi + # (and take pseudocounts into account): + # (k+1)/(n+2)=psi -> k = psi*(n+2) - 1 + if(junction_k == junction){ + new_psi <- junction_psi + injDpsi + new_k <- round( new_psi * + (n_ji + 2*pseudocount()) - pseudocount() ) + + # store position of outlier + indOut[i] <- injDirection + indexDpsi[i] <- injDpsi + } + else{ + deltaPSI_k <- - (psi[junction_k,sample] / + (1-junction_psi)) * injDpsi + new_psi <- psi[junction_k,sample] + deltaPSI_k + new_k <- round( new_psi*(n_ji + 2*pseudocount()) - + pseudocount() ) + + # also store position of other junction used in swap + indOut[i] <- -injDirection * 2 + indexDpsi[i] <- deltaPSI_k + } + # for SE: ensure new_k <= n_ij + # (so that o=n-k is always >= 0) + # (not needed for psi3/5 because o + # will recalculated from k's there) + if(length(group_junctions)==1){ # if(type == "psiSite"){ + new_k <- min(new_k, n_ji) + } + # ensure new_k >= 0 and assign k_ij <- new_k + newKs[i] <- max(0, new_k) + } + + if(verbose){ + print(paste("injected outlier", j, "with delta PSI of", + injDpsi, "at junction", junction, "and sample", + sample)) + } + + return(list(newKs = newKs, newOs=sum(newKs)-newKs, + junctions = group_junctions, injDeltaPSI = indexDpsi, + injDirections = indOut, + sample = rep(sample, length(group_junctions)))) + }) + + # get all junctions, samples, ... where outlier injection changed the counts + junctions <- unlist(lapply(result, "[[", 'junctions')) + samples <- unlist(lapply(result, "[[", 'sample')) + newKs <- unlist(lapply(result, "[[", 'newKs')) + newOs <- unlist(lapply(result, "[[", 'newOs')) + injDirection <- unlist(lapply(result, "[[", 'injDirections')) + injDeltaPSI <- unlist(lapply(result, "[[", 'injDeltaPSI')) + + # matrices to store indices of outliers and their delta psi + indexOut <- matrix(0, nrow = j, ncol = m) + indexDeltaPSI <- matrix(0, nrow = j, ncol = m) + # set counts to changed counts after the injection + replaceIndices <- matrix(c(junctions,samples), ncol=2) + k[replaceIndices] <- newKs + if(length(available_groups) == j){ + o <- n - k + } else{ + o[replaceIndices] <- newOs + } + indexOut[replaceIndices] <- injDirection + indexDeltaPSI[replaceIndices] <- injDeltaPSI + # store positions of true outliers and their true delta PSI value setAssayMatrix(fds=fds, name="trueOutliers", type=type, - withDimnames=FALSE) <- indexOut + withDimnames=FALSE) <- indexOut setAssayMatrix(fds=fds, name="trueDeltaPSI", type=type, - withDimnames=FALSE) <- indexDeltaPsi - + withDimnames=FALSE) <- indexDeltaPSI + + # store new k counts which include the outlier counts + counts(fds, type=type, side="ofInterest", withDimnames=FALSE) <- k + + # store modified other counts + counts(fds, type=type, side="other", withDimnames=FALSE) <- o + return(fds) } - removeInjectedOutliers <- function(fds, type){ - + # copy injected k and o counts if(type == "psiSite"){ - setAssayMatrix(fds, type="psiSite", "outlierCounts") <- - counts(fds, type="psiSite", side="ofInterest") - setAssayMatrix(fds, type="psiSite", "outlierOtherCounts") <- - counts(fds, type="psiSite", side="other") + setAssayMatrix(fds, type="psiSite", "outlierCounts", + withDimnames=FALSE) <- counts(fds, type="psiSite", side="ofInteres") + setAssayMatrix(fds, type="psiSite", "outlierOtherCounts", + withDimnames=FALSE) <- counts(fds, type="psiSite", side="other") } else{ - setAssayMatrix(fds, type="psi5", "outlierCounts") <- - counts(fds, type="psi5", side="ofInterest") - setAssayMatrix(fds, type="psi5", "outlierOtherCounts") <- - counts(fds, type="psi5", side="other") - setAssayMatrix(fds, type="psi3", "outlierOtherCounts") <- - counts(fds, type="psi3", side="other") + setAssayMatrix(fds, type="psi5", "outlierCounts", + withDimnames=FALSE) <- counts(fds, type="psi5", side="ofInterest") + setAssayMatrix(fds, type="psi5", "outlierOtherCounts", + withDimnames=FALSE) <- counts(fds, type="psi5", side="other") + setAssayMatrix(fds, type="psi3", "outlierOtherCounts", + withDimnames=FALSE) <- counts(fds, type="psi3", side="other") } - + # assign original k and o to rawCountsJ and rawOtherCounts if(type == "psiSite"){ - counts(fds, type="psiSite", side="ofInterest") <- - getAssayMatrix(fds, type="psiSite", "originalCounts") - counts(fds, type="psiSite", side="other") <- - getAssayMatrix(fds, type="psiSite", "originalOtherCounts") - + counts(fds, type="psiSite", side="ofInterest", withDimnames=FALSE) <- + getAssayMatrix(fds, type="psiSite", "originalCounts") + counts(fds, type="psiSite", side="other", withDimnames=FALSE) <- + getAssayMatrix(fds, type="psiSite", "originalOtherCounts") + assays(fds)[['originalCounts_psiSite']] <- NULL assays(fds)[['originalOtherCounts_psiSite']] <- NULL } else{ - counts(fds, type="psi5", side="ofInterest") <- - getAssayMatrix(fds, type="psi5", "originalCounts") - counts(fds, type="psi5", side="other") <- - getAssayMatrix(fds, type="psi5", "originalOtherCounts") - counts(fds, type="psi3", side="other") <- - getAssayMatrix(fds, type="psi3", "originalOtherCounts") - + counts(fds, type="psi5", side="ofInterest", withDimnames=FALSE) <- + getAssayMatrix(fds, type="psi5", "originalCounts") + counts(fds, type="psi5", side="other", withDimnames=FALSE) <- + getAssayMatrix(fds, type="psi5", "originalOtherCounts") + counts(fds, type="psi3", side="other", withDimnames=FALSE) <- + getAssayMatrix(fds, type="psi3", "originalOtherCounts") + assays(fds)[['originalCounts_psi5']] <- NULL assays(fds)[['originalOtherCounts_psi5']] <- NULL assays(fds)[['originalOtherCounts_psi3']] <- NULL } - + return(fds) - + } restoreInjectedOutliers <- function(fds, type){ - + # copy original k and o counts if(type == "psiSite"){ setAssayMatrix(fds, type="psiSite", "originalCounts") <- @@ -736,14 +757,14 @@ restoreInjectedOutliers <- function(fds, type){ setAssayMatrix(fds, type="psi3", "originalOtherCounts") <- counts(fds, type="psi3", side="other") } - + # assign injected k and o to rawCountsJ and rawOtherCounts if(type == "psiSite"){ counts(fds, type="psiSite", side="ofInterest") <- getAssayMatrix(fds, type="psiSite", "outlierCounts") counts(fds, type="psiSite", side="other") <- getAssayMatrix(fds, type="psiSite", "outlierOtherCounts") - + assays(fds)[['outlierCounts_psiSite']] <- NULL assays(fds)[['outlierOtherCounts_psiSite']] <- NULL } @@ -754,14 +775,14 @@ restoreInjectedOutliers <- function(fds, type){ getAssayMatrix(fds, type="psi5", "outlierOtherCounts") counts(fds, type="psi3", side="other") <- getAssayMatrix(fds, type="psi3", "outlierOtherCounts") - + assays(fds)[['outlierCounts_psi5']] <- NULL assays(fds)[['outlierOtherCounts_psi5']] <- NULL assays(fds)[['outlierOtherCounts_psi3']] <- NULL } - + return(fds) - + } diff --git a/R/plotMethods.R b/R/plotMethods.R index cd568ad5..0dacc3e5 100644 --- a/R/plotMethods.R +++ b/R/plotMethods.R @@ -1003,7 +1003,9 @@ getColDataAsDFFactors <- function(fds, names){ } #' @noRd -qlogisWithCap <- function(x){ +qlogisWithCap <- function(x, digits=2){ + x <- round(x, digits) + x <- pmin(pmax(x, 10^-digits), 1-10^-digits) ans <- qlogis(x) ans[is.infinite(ans)] <- NA rowm <- rowMaxs(ans, na.rm=TRUE) diff --git a/R/pvalsNzscore.R b/R/pvalsNzscore.R index a468fc67..28bddb33 100644 --- a/R/pvalsNzscore.R +++ b/R/pvalsNzscore.R @@ -27,7 +27,7 @@ calculateZscore <- function(fds, type=currentType(fds), logit=TRUE){ zscores <- (residual - rowMeans(residual)) / rowSds(residual) zScores(fds, withDimnames=FALSE) <- zscores - + return(fds) } @@ -59,7 +59,8 @@ calculatePvalues <- function(fds, type=currentType(fds), fwer_pval <- bplapply(seq_col(pvals), adjust_FWER_PValues, pvals=pvals, index, BPPARAM=BPPARAM) fwer_pvals <- do.call(cbind, fwer_pval) - pVals(fds, type=type, dist="BetaBinomial") <- fwer_pvals + pVals(fds, type=type, dist="BetaBinomial", + withDimnames=FALSE) <- fwer_pvals return(fds) } diff --git a/R/saveHDF5Objects.R b/R/saveHDF5Objects.R index a40552e6..3dedee6c 100644 --- a/R/saveHDF5Objects.R +++ b/R/saveHDF5Objects.R @@ -66,7 +66,7 @@ loadFraserDataSet <- function(dir, name=NULL, file=NULL, upgrade=FALSE){ } fds <- readRDS(fdsFile) - # needs to be here due to our FRASER -> FRASER package change. + # needs to be here due to our FraseR -> FRASER package change. # can be removed later if the full pipeline is rerun attributes(fds)$class <- structure("FraserDataSet", package="FRASER") @@ -97,7 +97,7 @@ loadFraserDataSet <- function(dir, name=NULL, file=NULL, upgrade=FALSE){ # set the correct path of the assay seed file (if folder changed) for(aname in assayNames(fds)){ - if(is(assay(fds, aname), "matrix")){ + if(is(assay(fds, aname, withDimnames=FALSE), "matrix")){ next } message("Loading assay: ", aname) @@ -105,9 +105,14 @@ loadFraserDataSet <- function(dir, name=NULL, file=NULL, upgrade=FALSE){ if(!file.exists(afile)){ warning(paste("Can not find assay file: ", aname, ".", "The assay will be removed from the object.")) - assay(fds, aname) <- NULL - } else if(afile != path(assay(fds, aname))) { - path(assay(fds, aname)) <- afile + assay(fds, aname, withDimnames=FALSE) <- NULL + } else if(afile != path(assay(fds, aname, withDimnames=FALSE))) { + if(R.Version()$major == "3"){ + path(assay(fds, aname, withDimnames=FALSE)) <- afile + } else { + slot(assay(fds, aname, withDimnames=FALSE), + "seed")@seed@filepath <- afile + } } } @@ -131,6 +136,7 @@ saveFraserDataSet <- function(fds, dir=NULL, name=NULL, rewrite=FALSE) { # over each assay object name(fds) <- name + workingDir(fds) <- dir for(aname in assayNames(fds)){ assay <- assay(fds, aname) assay(fds, aname, withDimnames=FALSE) <- saveAsHDF5( diff --git a/R/updateRho.R b/R/updateRho.R index 54d8b9dc..f5a0d429 100644 --- a/R/updateRho.R +++ b/R/updateRho.R @@ -15,7 +15,8 @@ updateRho <- function(fds, type, rhoRange, BPPARAM, verbose){ double(1), "minimum") if(isTRUE(verbose)){ - print(summary(rho(fds))) + stxt <- capture.output(summary(rho(fds))) + message(date(), ": rho fit:\n\t", paste(stxt, collapse="\n\t")) } validObject(fds) diff --git a/appveyor.yml b/appveyor.yml index e47fd258..1f318d9f 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -35,10 +35,13 @@ environment: # BIOC_USE_DEVEL: TRUE # GCC_PATH: mingw_32 # R_ARCH: i386 -# - R_VERSION: devel -# BIOC_USE_DEVEL: TRUE + - R_VERSION: devel + BIOC_USE_DEVEL: TRUE - R_VERSION: release - - R_VERSION: 4.0.0 + # dont test 4.0.0 due to: + # * https://github.com/r-lib/roxygen2/issues/1128 + # * https://github.com/RcppCore/Rcpp/issues/1095 + - R_VERSION: 4.0.2 - R_VERSION: 3.6.0 build_script: diff --git a/inst/extdata/bam/sample1.bam b/inst/extdata/bam/sample1.bam index 7e2e9316eeb71bfa10d78c4f31a66824cc58a4bf..522026bdec6ed2efdb99540824cc8526e0f24869 100644 GIT binary patch delta 48482 zcmV)VK(D{qpaal_1AiZj2m}BC000301^_}s0sssFb8_@$+`!1d;NaoH8RlnYsAs|% z>~EEvpI?-cnU`3S%HoqepG%@FL2yo~0bg>E!aSY<*atH!*41Y}xEDR0eT?`BiO)TRL z3=PfV9i7cB10o&$A|m38<4xj$GMvG_R>g*#K2BCXnMsD60X|lqIXR`dKz9;rj3IT5 zF{X|&Mud!U_U9}uPAyVM&d<$F%u7+o$;?Z&QUC=U7c&C`11k`N;)OwO>RE0!2*=Qp 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user can create several analysis plots directly from the fitted \fds{} object. These plotting functions are described in section \ref{sec:result-vis}. -<>= +<>= # load FRASER library library(FRASER) @@ -214,10 +219,10 @@ fds <- annotateRangesWithTxDb(fds, txdb=txdb, orgDb=orgDb) # alternatively, we also provide a way to use biomart for the annotation: # fds <- annotateRanges(fds) -# get results: usually, using p-value cutoff is recommended, but on the small -# example dataset, this would give no results, so we use z-scores here instead -# res <- results(fds, zScoreCutoff=NA, padjCutoff=0.05, deltaPsiCutoff=0.1) -res <- results(fds, zScoreCutoff=1, padjCutoff=NA, deltaPsiCutoff=0.02) +# get results: we recommend to use an FDR cutoff 0.05, but due to the small +# dataset size we extract all events and their associated values +# eg: res <- results(fds, zScoreCutoff=NA, padjCutoff=0.05, deltaPsiCutoff=0.3) +res <- results(fds, zScoreCutoff=NA, padjCutoff=NA, deltaPsiCutoff=NA) res # result visualization @@ -323,7 +328,7 @@ resulting \Rclass{FraserDataSet} object contains two \Rclass{SummarizedExperiment} objects for each the junctions and the splice sites. -<>= +<>= # example of how to use parallelization: use 10 cores or the maximal number of # available cores if fewer than 10 are available and use Snow if on Windows if(.Platform$OS.type == "unix") { @@ -331,7 +336,9 @@ if(.Platform$OS.type == "unix") { } else { register(SnowParam(workers=min(10, multicoreWorkers()))) } +@ +<>= # count reads fds <- countRNAData(settings) fds @@ -345,16 +352,16 @@ together with the sample annotation from above to create the \fds: <>= # example sample annoation for precalculated count matrices sampleTable <- fread(system.file("extdata", "sampleTable_countTable.tsv", - package="FRASER", mustWork=TRUE)) + package="FRASER", mustWork=TRUE)) head(sampleTable) # get raw counts junctionCts <- fread(system.file("extdata", "raw_junction_counts.tsv.gz", - package="FRASER", mustWork=TRUE)) + package="FRASER", mustWork=TRUE)) head(junctionCts) spliceSiteCts <- fread(system.file("extdata", "raw_site_counts.tsv.gz", - package="FRASER", mustWork=TRUE)) + package="FRASER", mustWork=TRUE)) head(spliceSiteCts) # create FRASER object