TENxMatrix-class: 10x Genomics datasets as DelayedMatrix objects
In Bioconductor/HDF5Array: HDF5 datasets as array-like objects in R

TENxMatrix-class

R Documentation

10x Genomics datasets as DelayedMatrix objects

Description

A 10x Genomics dataset like the "1.3 Million Brain Cell Dataset" is an HDF5 sparse matrix stored in CSR/CSC/Yale format ("Compressed Sparse Row").

The TENxMatrix class is a DelayedMatrix subclass for representing and operating on this kind of dataset.

All the operations available for DelayedMatrix objects work on TENxMatrix objects.

Usage

## Constructor function:
TENxMatrix(filepath, group="matrix")

Arguments

`filepath`	The path (as a single string) to the HDF5 file where the 10x Genomics dataset is located.
`group`	The name of the group in the HDF5 file containing the 10x Genomics data.

Details

In addition to all the methods defined for DelayedMatrix objects, TENxMatrix objects support the following specialized methods: nzcount() and extractNonzeroDataByCol(). See ?H5SparseMatrixSeed for more information about what these methods do.

Value

TENxMatrix() returns a TENxMatrix object.

Note

If your dataset uses the HDF5 sparse matrix representation from 10x Genomics, use the TENxMatrix() constructor documented here.

But if your dataset uses the conventional (a.k.a. dense) HDF5 representation, use the HDF5Array() constructor instead.

Examples

## ---------------------------------------------------------------------
## SIMPLE TENxMatrix EXAMPLE
## ---------------------------------------------------------------------

sm <- Matrix::rsparsematrix(10, 7, density=0.3)
M <- writeTENxMatrix(sm)
M

class(M)  # TENxMatrix
is(M, "DelayedMatrix")  # TRUE

seed(M)
class(seed(M))  # TENxMatrixSeed

rhdf5::h5ls(path(M))

is_sparse(M)  # TRUE

## Use coercion to load the full dataset into memory:
as.matrix(M)          # as ordinary array (usually not recommended)
as(M, "dgCMatrix")    # as dgCMatrix (brings back 'sm')
as(M, "SparseArray")  # as SparseArray object (most efficient)
SparseArray(M)        # equivalent to 'as(M, "SparseArray")'

## ---------------------------------------------------------------------
## THE "1.3 Million Brain Cell Dataset" AS A DelayedMatrix OBJECT
## ---------------------------------------------------------------------

## The 1.3 Million Brain Cell Dataset from 10x Genomics is available
## via ExperimentHub:

library(ExperimentHub)
hub <- ExperimentHub()
query(hub, "TENxBrainData")
fname <- hub[["EH1039"]]

## 'fname' is an HDF5 file. Use h5ls() to list its content:
h5ls(fname)

## The 1.3 Million Brain Cell Dataset is represented by the "mm10"
## group. We point the TENxMatrix() constructor to this group to
## create a TENxMatrix object representing the dataset:
oneM <- TENxMatrix(fname, group="mm10")
oneM

is(oneM, "DelayedMatrix")  # TRUE
seed(oneM)
path(oneM)
nzcount(oneM)  # nb of nonzero values in the dataset

## Some examples of delayed operations:
oneM != 0
oneM^2

## ---------------------------------------------------------------------
## SOME EXAMPLES OF ROW/COL SUMMARIZATION
## ---------------------------------------------------------------------

## In order to reduce computation times, we'll use only the first
## 25000 columns of the 1.3 Million Brain Cell Dataset:
oneM25k <- oneM[ , 1:25000]

## Row/col summarization methods like rowSums() use a block-processing
## mechanism behind the scene that can be controlled via global
## settings. 2 important settings that can have a strong impact on
## performance are the automatic number of workers and automatic block
## size, controlled by setAutoBPPARAM() and setAutoBlockSize()
## respectively.
library(BiocParallel)
if (.Platform$OS.type != "windows") {
    ## On a modern Linux laptop with 8 cores (as reported by
    ## parallel::detectCores()) and 16 Gb of RAM, reasonably good
    ## performance is achieved by setting the automatic number of workers
    ## to 5 or 6 and the automatic block size between 300 Mb and 400 Mb:
    workers <- 5
    block_size <- 3e8  # 300 Mb
    setAutoBPPARAM(MulticoreParam(workers))
} else {
    ## MulticoreParam() is not supported on Windows so we use SnowParam()
    ## on this platform. Also we reduce the block size to 200 Mb on
    ## 32-bit Windows to avoid memory allocation problems (they tend to
    ## be common there because a process cannot use more than 3 Gb of
    ## memory).
    workers <- 4
    setAutoBPPARAM(SnowParam(workers))
    block_size <- if (.Platform$r_arch == "i386") 2e8 else 3e8
}
setAutoBlockSize(block_size)

## We're ready to compute the library sizes, number of genes expressed
## per cell, and average expression across cells:
system.time(lib_sizes <- colSums(oneM25k))
system.time(n_exprs <- colSums(oneM25k != 0))
system.time(ave_exprs <- rowMeans(oneM25k))

## Note that the 3 computations above load the data in oneM25k 3 times
## in memory. This can be avoided by computing the 3 summarizations in
## a single pass with blockApply(). First we define the function that
## we're going to apply to each block of data:
FUN <- function(block)
  list(colSums(block), colSums(block != 0), rowSums(block))

## Then we call blockApply() to apply FUN() to each block. The blocks
## are defined by the grid passed to the 'grid' argument. In this case
## we supply a grid made with colAutoGrid() to generate blocks of full
## columns (see ?colAutoGrid for more information):
system.time({
  block_results <- blockApply(oneM25k, FUN, grid=colAutoGrid(oneM25k),
                              verbose=TRUE)
})

## 'block_results' is a list with 1 list element per block in
## colAutoGrid(oneM25k). Each list element is the result that was
## obtained by applying FUN() on the block so is itself a list of
## length 3.
## Let's combine the results:
lib_sizes2 <- unlist(lapply(block_results, `[[`, 1L))
n_exprs2 <- unlist(lapply(block_results, `[[`, 2L))
block_rowsums <- unlist(lapply(block_results, `[[`, 3L), use.names=FALSE)
tot_exprs <- rowSums(matrix(block_rowsums, nrow=nrow(oneM25k)))
ave_exprs2 <- setNames(tot_exprs / ncol(oneM25k), rownames(oneM25k))

## Sanity checks:
stopifnot(all.equal(lib_sizes, lib_sizes2))
stopifnot(all.equal(n_exprs, n_exprs2))
stopifnot(all.equal(ave_exprs, ave_exprs2))

## Turn off parallel evaluation and reset automatic block size to factory
## settings:
setAutoBPPARAM()
setAutoBlockSize()

## ---------------------------------------------------------------------
## extractNonzeroDataByCol()
## ---------------------------------------------------------------------

## extractNonzeroDataByCol() provides a convenient and very efficient
## way to extract the nonzero data in a compact form:
nonzeros <- extractNonzeroDataByCol(oneM, 1:25000)  # takes < 5 sec.

## The data is returned as an IntegerList object with one list element
## per column and no row indices associated to the values in the object.
## Furthermore, the values within a given list element can be returned
## in any order:
nonzeros

names(nonzeros) <- colnames(oneM25k)

## This can be used to compute some simple summaries like the library
## sizes and the number of genes expressed per cell. For these use
## cases, it is a lot more efficient than using colSums(oneM25k) and
## colSums(oneM25k != 0):
lib_sizes3 <- sum(nonzeros)
n_exprs3 <- lengths(nonzeros)

## Sanity checks:
stopifnot(all.equal(lib_sizes, lib_sizes3))
stopifnot(all.equal(n_exprs, n_exprs3))

Bioconductor/HDF5Array documentation built on Nov. 30, 2024, 3:14 a.m.