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readAlevin <- function(files, dropInfReps, filterBarcodes, tierImport, forceSlow, dropMeanVar) {
dir <- sub("/alevin$","",dirname(files))
barcode.file <- file.path(dir, "alevin/quants_mat_rows.txt")
gene.file <- file.path(dir, "alevin/quants_mat_cols.txt")
matrix.file <- file.path(dir, "alevin/quants_mat.gz")
tier.file <- file.path(dir, "alevin/quants_tier_mat.gz")
mean.file <- file.path(dir, "alevin/quants_mean_mat.gz")
var.file <- file.path(dir, "alevin/quants_var_mat.gz")
boot.file <- file.path(dir, "alevin/quants_boot_mat.gz")
boot.barcode.file <- file.path(dir, "alevin/quants_boot_rows.txt")
whitelist.file <- file.path(dir, "alevin/whitelist.txt")
for (f in c(barcode.file, gene.file, matrix.file)) {
if (!file.exists(f)) {
stop("expecting 'files' to point to 'quants_mat.gz' file in a directory 'alevin'
also containing 'quants_mat_rows.txt' and 'quant_mat_cols.txt'.
please re-run alevin preserving output structure")
}
}
cell.names <- readLines(barcode.file)
gene.names <- readLines(gene.file)
if (!requireNamespace("jsonlite", quietly=TRUE)) {
stop("importing alevin requires package `jsonlite`")
}
jsonPath <- file.path(dir, "cmd_info.json")
cmd_info <- jsonlite::fromJSON(jsonPath)
if ("numCellBootstraps" %in% names(cmd_info)) {
num.boot <- as.numeric(cmd_info$numCellBootstraps)
} else {
num.boot <- 0
}
if (!requireNamespace("Matrix", quietly=TRUE)) {
stop("importing alevin requires package `Matrix`")
}
# test for fishpond >= 1.1.17
hasFishpond <- TRUE
if (!requireNamespace("fishpond", quietly=TRUE)) {
hasFishpond <- FALSE
} else {
if (packageVersion("fishpond") < "1.1.18") {
hasFishpond <- FALSE
}
}
# for testing purposes, force the use of the slower R code for importing alevin
if (forceSlow) {
hasFishpond <- FALSE
}
if (!hasFishpond) {
message("importing alevin data is much faster after installing `fishpond` (>= 1.2.0)")
if (tierImport) stop("tierImport=TRUE requires fishpond package")
}
extraMsg <- if (hasFishpond) "with fishpond" else ""
message(paste("reading in alevin gene-level counts across cells", extraMsg))
if (hasFishpond) {
# reads alevin's Efficient Data Storage (EDS) format
# using C++ code in the fishpond package
mat <- readAlevinFast(matrix.file, gene.names, cell.names)
if (tierImport) {
tier <- readAlevinFast(tier.file, gene.names, cell.names, tierImport=TRUE)
}
} else {
# reads alevin EDS format in R, using e.g. `readBin` and `intToBits`
# slow in R, because requires looping over cells to read positions and expression
mat <- readAlevinBits(matrix.file, gene.names, cell.names)
}
# cell barcode filtering
if (filterBarcodes & file.exists(whitelist.file)) {
filter <- readLines(whitelist.file)
keep <- colnames(mat) %in% filter
message(paste("filtering down to",sum(keep),"cell barcodes"))
mat <- mat[,keep]
if (tierImport) {
tier <- tier[,keep]
}
}
# read in inferential replicate data
if (num.boot > 0 & !dropMeanVar) {
message(paste("reading in alevin inferential variance", extraMsg))
mean.exists <- file.exists(mean.file)
var.exists <- file.exists(var.file)
boot.exists <- file.exists(boot.file)
stopifnot(mean.exists)
stopifnot(var.exists)
boot.cell.names <- readLines(boot.barcode.file)
if (hasFishpond) {
mean.mat <- readAlevinFast(mean.file, gene.names, boot.cell.names)
var.mat <- readAlevinFast(var.file, gene.names, boot.cell.names)
} else {
mean.mat <- readAlevinBits(mean.file, gene.names, boot.cell.names)
var.mat <- readAlevinBits(var.file, gene.names, boot.cell.names)
}
# need to re-arrange to match the counts matrix
mean.mat <- mean.mat[,cell.names]
var.mat <- var.mat[,cell.names]
# cell barcode filtering of bootstrap mean and variance matrices
if (filterBarcodes & file.exists(whitelist.file)) {
mean.mat <- mean.mat[,keep]
var.mat <- var.mat[,keep]
}
if (boot.exists & !dropInfReps) {
# read in bootstrap inferential replicates
message("reading in alevin inferential replicates (set dropInfReps=TRUE to skip)")
infReps <- readAlevinInfReps(boot.file, gene.names, boot.cell.names, num.boot)
# need to re-arrange to match the counts matrix
infReps <- lapply(infReps, function(z) z[,cell.names])
# cell barcode filtering of infReps
if (filterBarcodes & file.exists(whitelist.file)) {
infReps <- lapply(infReps, function(z) z[,keep])
}
# TODO this is a mess, benchmark if growing a list is really a problem
# with inf reps
if (tierImport) {
return(list(counts=mat, tier=tier,
mean=mean.mat, variance=var.mat,
infReps=infReps))
} else {
return(list(counts=mat, mean=mean.mat, variance=var.mat, infReps=infReps))
}
} else {
# without inf reps
if (tierImport) {
return(list(counts=mat, tier=tier, mean=mean.mat, variance=var.mat))
} else {
return(list(counts=mat, mean=mean.mat, variance=var.mat))
}
}
} else {
# without inferential replicate data at all
if (tierImport) {
return(list(counts=mat, tier=tier))
} else {
return(mat)
}
}
}
getAlevinVersion <- function(files) {
if (!requireNamespace("jsonlite", quietly=TRUE)) {
stop("importing Alevin quantification requires package `jsonlite`")
}
fish_dir <- dirname(dirname(files))
jsonPath <- file.path(fish_dir, "cmd_info.json")
cmd_info <- jsonlite::fromJSON(jsonPath)
cmd_info$salmon_version
}
# this is the R (slow) version of the reader for alevin's EDS format,
# see below for another function that leverages C++ code from fishpond::readEDS()
readAlevinBits <- function(matrix.file, gene.names, cell.names) {
num.cells <- length(cell.names)
num.genes <- length(gene.names)
len.bit.vec <- ceiling(num.genes/8)
# the bit vector matrix is 8 rows x num.genes/8 columns
# and stores the positions of the non-zero counts
bits.mat <- matrix(nrow=8, ncol=num.cells * len.bit.vec)
con <- gzcon(file(matrix.file, "rb"))
for (j in seq_len(num.cells)) {
# read the bit vectors
ints <- readBin(con, integer(), size=1, signed=FALSE, endian="little", n=len.bit.vec)
bits <- matrix(intToBits(ints), nrow=32)
mode(bits) <- "integer"
# 8 to 1, because intToBits gives the least sig bit first
bits <- bits[8:1,]
num.exp.genes <- sum(bits == 1)
# store bits in the matrix
idx <- (j-1) * len.bit.vec + seq_len(len.bit.vec)
bits.mat[,idx] <- bits
# read in counts, but don't store
counts <- readBin(con, double(), size=4, endian="little", n=num.exp.genes)
}
close(con)
con <- gzcon(file(matrix.file, "rb"))
# stores all of the non-zero counts from all cells concatenated
counts.vec <- numeric(sum(bits.mat))
ptr <- 0
for (j in seq_len(num.cells)) {
# read in bit vectors, but don't store
ints <- readBin(con, integer(), size=1, signed=FALSE, endian="little", n=len.bit.vec)
bit.idx <- (j-1) * len.bit.vec + seq_len(len.bit.vec)
num.exp.genes <- sum(bits.mat[,bit.idx])
cts.idx <- ptr + seq_len(num.exp.genes)
counts.vec[cts.idx] <- readBin(con, double(), size=4, endian="little", n=num.exp.genes)
ptr <- ptr + num.exp.genes
}
close(con)
gene.idx <- lapply(seq_len(num.cells), function(j) {
idx <- (j-1) * len.bit.vec + seq_len(len.bit.vec)
which(head(as.vector(bits.mat[,idx]), num.genes) == 1)
})
len.gene.idx <- lengths(gene.idx)
cell.idx <- rep(seq_along(len.gene.idx), len.gene.idx)
# build sparse matrix
mat <- Matrix::sparseMatrix(i=unlist(gene.idx),
j=cell.idx,
x=counts.vec,
dims=c(num.genes, num.cells),
dimnames=list(gene.names, cell.names),
giveCsparse=FALSE)
mat
}
# this function performs the same operation as the above R code,
# reading in alevin's EDS format and creating a sparse matrix,
# but it leverages the C++ code in fishpond::readEDS()
readAlevinFast <- function(matrix.file, gene.names, cell.names, tierImport=FALSE) {
num.genes <- length(gene.names)
num.cells <- length(cell.names)
mat <- fishpond::readEDS(num.genes, num.cells, matrix.file, tierImport)
dimnames(mat) <- list(gene.names, cell.names)
mat
}
readAlevinInfReps <- function(boot.file, gene.names, cell.names, num.boot) {
num.cells <- length(cell.names)
num.genes <- length(gene.names)
len.bit.vec <- ceiling(num.genes/8)
# a list to store the bit vector matrices
# each element of the list is for an inf rep
# (for description of `bits.mat` see the readAlevinBits() function)
bits.mat.list <- lapply(seq_len(num.boot), function(i)
matrix(nrow=8, ncol=num.cells * len.bit.vec))
con <- gzcon(file(boot.file, "rb"))
for (j in seq_len(num.cells)) {
for (i in seq_len(num.boot)) {
# read the bit vectors
ints <- readBin(con, integer(), size=1, signed=FALSE, endian="little", n=len.bit.vec)
bits <- matrix(intToBits(ints), nrow=32)
mode(bits) <- "integer"
# 8 to 1, because intToBits gives the least sig bit first
bits <- bits[8:1,]
num.exp.genes <- sum(bits == 1)
# store bits in the matrix
idx <- (j-1) * len.bit.vec + seq_len(len.bit.vec)
bits.mat.list[[i]][,idx] <- bits
# read in counts, but don't store
counts <- readBin(con, double(), size=4, endian="little", n=num.exp.genes)
}
}
close(con)
con <- gzcon(file(boot.file, "rb"))
# as with `bits.mat.list`, `counts.vec.list` is a list
# over inferential replicates, each one is a counts vector
# storing all of the non-zero counts from all cells concatenated
counts.vec.list <- lapply(seq_len(num.boot), function(i)
numeric(sum(bits.mat.list[[i]])))
ptr <- numeric(num.boot)
for (j in seq_len(num.cells)) {
for (i in seq_len(num.boot)) {
# read in bit vectors, but don't store
ints <- readBin(con, integer(), size=1, signed=FALSE, endian="little", n=len.bit.vec)
bit.idx <- (j-1) * len.bit.vec + seq_len(len.bit.vec)
num.exp.genes <- sum(bits.mat.list[[i]][,bit.idx])
cts.idx <- ptr[i] + seq_len(num.exp.genes)
counts.vec.list[[i]][cts.idx] <- readBin(con, double(), size=4, endian="little", n=num.exp.genes)
ptr[i] <- ptr[i] + num.exp.genes
}
}
close(con)
infReps <- lapply(seq_len(num.boot), function(i) {
gene.idx <- lapply(seq_len(num.cells), function(j) {
idx <- (j-1) * len.bit.vec + seq_len(len.bit.vec)
which(head(as.vector(bits.mat.list[[i]][,idx]), num.genes) == 1)
})
len.gene.idx <- lengths(gene.idx)
cell.idx <- rep(seq_along(len.gene.idx), len.gene.idx)
z <- Matrix::sparseMatrix(i=unlist(gene.idx),
j=cell.idx,
x=counts.vec.list[[i]],
dims=c(num.genes, num.cells),
dimnames=list(gene.names, cell.names),
giveCsparse=FALSE)
})
infReps
}
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