Exploring the 1.3 million brain cell scRNA-seq data from 10X Genomics
30 October 2021
Package
TENxBrainData 1.14.0
1 Exploring the 1.3 million brain cell scRNA-seq data from 10X Genomics
Package: TENxBrainData
Author: Aaron Lun ([email protected]), Martin Morgan
Modification date: 30 December, 2017
Compilation date: 2021-10-30
The TENxBrainData package provides a R /
Bioconductor resource for representing and manipulating the 1.3
million brain cell single-cell RNA-seq (scRNA-seq) data set generated
by 10X Genomics. It makes extensive use of the r Biocpkg("HDF5Array") package to avoid loading the entire data set in
memory, instead storing the counts on disk as a HDF5 file and loading
subsets of the data into memory upon request.
2 Initial work flow
2.1 Loading the data
We use the TENxBrainData function to download the relevant files
from Bioconductor’s ExperimentHub web resource. This includes the
HDF5 file containing the counts, as well as the metadata on the rows
(genes) and columns (cells). The output is a single
SingleCellExperiment object from the SingleCellExperiment
package. This is equivalent to a SummarizedExperiment class but
with a number of features specific to single-cell data.
library(TENxBrainData)
tenx <- TENxBrainData()
tenx## class: SingleCellExperiment
## dim: 27998 1306127
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(2): Ensembl Symbol
## colnames(1306127): AAACCTGAGATAGGAG-1 AAACCTGAGCGGCTTC-1 ...
## TTTGTCAGTTAAAGTG-133 TTTGTCATCTGAAAGA-133
## colData names(4): Barcode Sequence Library Mouse
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):The first call to TENxBrainData() will take some time due to the
need to download some moderately large files. The files are then
stored locally such that ensuing calls in the same or new sessions are
fast.
The count matrix itself is represented as a DelayedMatrix from the
DelayedArray package. This wraps the underlying HDF5
file in a container that can be manipulated in R. Each count
represents the number of unique molecular identifiers (UMIs) assigned
to a particular gene in a particular cell.
counts(tenx)## <27998 x 1306127> matrix of class DelayedMatrix and type "integer":
## AAACCTGAGATAGGAG-1 ... TTTGTCATCTGAAAGA-133
## [1,] 0 . 0
## [2,] 0 . 0
## [3,] 0 . 0
## [4,] 0 . 0
## [5,] 0 . 0
## ... . . .
## [27994,] 0 . 0
## [27995,] 1 . 0
## [27996,] 0 . 0
## [27997,] 0 . 0
## [27998,] 0 . 02.2 Exploring the data
To quickly explore the data set, we compute some summary statistics on the count matrix. We increase the DelayedArray block size to indicate that we can use up to 2 GB of memory for loading the data into memory from disk.
options(DelayedArray.block.size=2e9)We are interested in library sizes colSums(counts(tenx)), number of
genes expressed per cell colSums(counts(tenx) != 0), and average
expression across cells `rowMeans(counts(tenx)). A naive implement
might be
lib.sizes <- colSums(counts(tenx))
n.exprs <- colSums(counts(tenx) != 0L)
ave.exprs <- rowMeans(counts(tenx))However, the data is read from disk, disk access is comparatively slow, and the naive implementation reads the data three times. Instead, we’ll divide the data into column ‘chunks’ of about 10,000 cells; we do this on a subset of data to reduce computation time during the exploratory phase.
tenx20k <- tenx[, seq_len(20000)]
chunksize <- 5000
cidx <- snow::splitIndices(ncol(tenx20k), ncol(tenx20k) / chunksize)and iterate through the file reading the data and accumulating statistics on each iteration.
lib.sizes <- n.exprs <- numeric(ncol(tenx20k))
tot.exprs <- numeric(nrow(tenx20k))
for (i in head(cidx, 2)) {
message(".", appendLF=FALSE)
m <- as.matrix(counts(tenx20k)[,i, drop=FALSE])
lib.sizes[i] <- colSums(m)
n.exprs[i] <- colSums(m != 0)
tot.exprs <- tot.exprs + rowSums(m)
}
ave.exprs <- tot.exprs / ncol(tenx20k)Since the calculations are expensive and might be useful in the
future, we annotate the tenx20k object
colData(tenx20k)$lib.sizes <- lib.sizes
colData(tenx20k)$n.exprs <- n.exprs
rowData(tenx20k)$ave.exprs <- ave.exprsLibrary sizes follow an approximately log normal distribution, and are surprisingly small.
hist(
log10(colData(tenx20k)$lib.sizes),
xlab=expression(Log[10] ~ "Library size"),
col="grey80"
)
Expression of only a few thousand genes are detected in each sample.
hist(colData(tenx20k)$n.exprs, xlab="Number of detected genes", col="grey80")
Average expression values (read counts) are small.
hist(
log10(rowData(tenx20k)$ave.exprs),
xlab=expression(Log[10] ~ "Average count"),
col="grey80"
)
We also examine the top most highly-expressing genes in this data set.
o <- order(rowData(tenx20k)$ave.exprs, decreasing=TRUE)
head(rowData(tenx20k)[o,])## DataFrame with 6 rows and 3 columns
## Ensembl Symbol ave.exprs
## <character> <array> <numeric>
## 1 ENSMUSG00000092341 Malat1 49.1875
## 2 ENSMUSG00000049775 Tmsb4x 24.3624
## 3 ENSMUSG00000072235 Tuba1a 23.2797
## 4 ENSMUSG00000064357 mt-Atp6 17.1341
## 5 ENSMUSG00000064358 mt-Co3 14.3276
## 6 ENSMUSG00000028832 Stmn1 13.9624More advanced analysis procedures are implemented in various
Bioconductor packages - see the SingleCell biocViews for more
details.
2.3 Saving computations
Saving the tenx object in a standard manner, e.g.,
destination <- tempfile()
saveRDS(tenx, file = destination)saves the row-, column-, and meta-data as an R object, and remembers
the location and subset of the HDF5 file from which the object is
derived. The object can be read into a new R session with
readRDS(destination), provided the HDF5 file remains in it’s
original location.
3 Improving computational performance
3.1 Parallel computation
Row and column summary statistics can be computed in parallel, for
instance using bpiterate() in the BiocParallel package. We load
the package and start 5 ‘snow’ workers (separate processes).
library(BiocParallel)
register(bpstart(SnowParam(5)))This function requires an iterator to generate chunks of
data. Our iterator returns a function that itself returns the start
and end column indexes of each chunk, until there are no more chunks.
iterator <- function(tenx, cols_per_chunk = 5000, n = Inf) {
start <- seq(1, ncol(tenx), by = cols_per_chunk)
end <- c(tail(start, -1) - 1L, ncol(tenx))
n <- min(n, length(start))
i <- 0L
function() {
if (i == n)
return(NULL)
i <<- i + 1L
c(start[i], end[i])
}
}Here is the iterator in action
iter <- iterator(tenx)
iter()## [1] 1 5000iter()## [1] 5001 10000iter()## [1] 10001 15000bpiterate() requires a function that acts on each data chunk. It
receives the output of the iterator, as well as any other arguments it
may require, and returns the summary statistics for that chunk
fun <- function(crng, counts, ...) {
## `fun()` needs to be self-contained for some parallel back-ends
suppressPackageStartupMessages({
library(TENxBrainData)
})
m <- as.matrix( counts[ , seq(crng[1], crng[2]) ] )
list(
row = list(
n = rowSums(m != 0), sum = rowSums(m), sumsq = rowSums(m * m)
),
column = list(
n = colSums(m != 0), sum = colSums(m), sumsq = colSums(m * m)
)
)
}We can test this function as
res <- fun( iter(), unname(counts(tenx)) )
str(res)## List of 2
## $ row :List of 3
## ..$ n : num [1:27998] 114 0 0 4 0 ...
## ..$ sum : num [1:27998] 120 0 0 4 0 ...
## ..$ sumsq: num [1:27998] 134 0 0 4 0 ...
## $ column:List of 3
## ..$ n : num [1:5000] 2077 2053 2503 1617 1402 ...
## ..$ sum : num [1:5000] 4740 4309 6652 2930 2408 ...
## ..$ sumsq: num [1:5000] 52772 32577 90380 19598 16622 ...Finally, bpiterate() requires a function to reduce succesive values
returned by fun()
reduce <- function(x, y) {
list(
row = Map(`+`, x$row, y$row),
column = Map(`c`, x$column, y$column)
)
}A test is
str( reduce(res, res) )## List of 2
## $ row :List of 3
## ..$ n : num [1:27998] 228 0 0 8 0 ...
## ..$ sum : num [1:27998] 240 0 0 8 0 ...
## ..$ sumsq: num [1:27998] 268 0 0 8 0 ...
## $ column:List of 3
## ..$ n : num [1:10000] 2077 2053 2503 1617 1402 ...
## ..$ sum : num [1:10000] 4740 4309 6652 2930 2408 ...
## ..$ sumsq: num [1:10000] 52772 32577 90380 19598 16622 ...Putting the pieces together and evaluating the first 25000 columns, we have
res <- bpiterate(
iterator(tenx, n = 5), fun, counts = unname(counts(tenx)),
REDUCE = reduce, reduce.in.order = TRUE
)
str(res)## List of 2
## $ row :List of 3
## ..$ n : num [1:27998] 579 1 0 29 0 ...
## ..$ sum : num [1:27998] 602 1 0 29 0 ...
## ..$ sumsq: num [1:27998] 652 1 0 29 0 ...
## $ column:List of 3
## ..$ n : num [1:25000] 1807 1249 2206 1655 3326 ...
## ..$ sum : num [1:25000] 4046 2087 4654 3193 8444 ...
## ..$ sumsq: num [1:25000] 35338 14913 31136 21619 106780 ...3.2 Working with Rle-compressed HDF5 data
The 10x Genomics data is also distributed in a compressed format, available from ExperimentHub
library(ExperimentHub)
hub <- ExperimentHub()
query(hub, "TENxBrainData")## ExperimentHub with 8 records
## # snapshotDate(): 2021-10-18
## # $dataprovider: 10X Genomics
## # $species: Mus musculus
## # $rdataclass: character
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH1039"]]'
##
## title
## EH1039 | Brain scRNA-seq data, 'HDF5-based 10X Genomics' format
## EH1040 | Brain scRNA-seq data, 'dense matrix' format
## EH1041 | Brain scRNA-seq data, sample (column) annotation
## EH1042 | Brain scRNA-seq data, gene (row) annotation
## EH1689 | Brain scRNA-seq data 20k subset, 'HDF5-based 10x Genomics' format
## EH1690 | Brain scRNA-seq data 20k subset, 'dense matrix' format
## EH1691 | Brain scRNA-seq data 20k subset, sample (column) annotation
## EH1692 | Brain scRNA-seq data 20k subset, gene (row) annotationfname <- hub[["EH1039"]]The structure of the file can be seen using the h5ls() command from
rhdf5.
h5ls(fname)## group name otype dclass dim
## 0 / mm10 H5I_GROUP
## 1 /mm10 barcodes H5I_DATASET STRING 1306127
## 2 /mm10 data H5I_DATASET INTEGER 2624828308
## 3 /mm10 gene_names H5I_DATASET STRING 27998
## 4 /mm10 genes H5I_DATASET STRING 27998
## 5 /mm10 indices H5I_DATASET INTEGER 2624828308
## 6 /mm10 indptr H5I_DATASET INTEGER 1306128
## 7 /mm10 shape H5I_DATASET INTEGER 2Non-zero counts are in the /mm10/data path. /mm10/indices
represent the row indices corresponding to each non-zero
count. /mm10/indptr divides the data and indices into successive
columns. For instance
start <- h5read(fname, "/mm10/indptr", start=1, count=25001)
head(start)## [1] 0 1807 3056 5262 6917 10243retrieves the offsets into /mm10/data of the first 25001 columns of
data. The offsets are 0-based because HDF5 use 0-based indexing; we
will sometimes need to add 1 to facilitate use in R.
Here we read the first 25000 columns of data into R, using data.table for efficient computation on this large data.
library(data.table)
dt <- data.table(
row = h5read(fname, "/mm10/indices", start = 1, count = tail(start, 1)) + 1,
column = rep(seq_len(length(start) - 1), diff(start)),
count = h5read(fname, "/mm10/data", start = 1, count = tail(start, 1))
)
dt## row column count
## 1: 27995 1 1
## 2: 27921 1 19
## 3: 27918 1 14
## 4: 27915 1 40
## 5: 27914 1 29
## ---
## 51028822: 63 25000 9
## 51028823: 39 25000 2
## 51028824: 38 25000 1
## 51028825: 17 25000 2
## 51028826: 8 25000 1Row and column summaries are then
dt[ ,
list(n = .N, sum = sum(count), sumsq = sum(count * count)),
keyby=row]## row n sum sumsq
## 1: 1 579 602 652
## 2: 2 1 1 1
## 3: 4 29 29 29
## 4: 6 215 615 3501
## 5: 7 5 5 5
## ---
## 20191: 27980 7 7 7
## 20192: 27994 22 22 22
## 20193: 27995 14773 28213 77081
## 20194: 27996 1946 2085 2393
## 20195: 27998 16 16 16dt[ ,
list(n = .N, sum = sum(count), sumsq = sum(count * count)),
keyby=column]## column n sum sumsq
## 1: 1 1807 4046 35338
## 2: 2 1249 2087 14913
## 3: 3 2206 4654 31136
## 4: 4 1655 3193 21619
## 5: 5 3326 8444 106780
## ---
## 24996: 24996 1142 2091 16827
## 24997: 24997 2610 5772 131212
## 24998: 24998 2209 4492 36600
## 24999: 24999 1049 1791 15795
## 25000: 25000 2015 4043 28809Iterating through 25000 columns of dense data took about 3 minutes of computational time (about 30 seconds elapsed time using 6 cores), compared to just a few seconds for sparse data. Processing the entire sparse data set would still require chunk-wise processing except on large-memory machines, and would benefit from parallel computation. In the later case, processing fewer than 25000 columns per chunk would reduce memory consumption of each chunk and hence allow more processing cores to operate, increasing overall processing speed.
4 Session information
sessionInfo()## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] data.table_1.14.2 ExperimentHub_2.2.0
## [3] AnnotationHub_3.2.0 BiocFileCache_2.2.0
## [5] dbplyr_2.1.1 BiocParallel_1.28.0
## [7] TENxBrainData_1.14.0 HDF5Array_1.22.0
## [9] rhdf5_2.38.0 DelayedArray_0.20.0
## [11] Matrix_1.3-4 SingleCellExperiment_1.16.0
## [13] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [15] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
## [17] IRanges_2.28.0 S4Vectors_0.32.0
## [19] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
## [21] matrixStats_0.61.0 knitr_1.36
## [23] BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 bit64_4.0.5
## [3] filelock_1.0.2 httr_1.4.2
## [5] tools_4.1.1 bslib_0.3.1
## [7] utf8_1.2.2 R6_2.5.1
## [9] DBI_1.1.1 rhdf5filters_1.6.0
## [11] withr_2.4.2 tidyselect_1.1.1
## [13] bit_4.0.4 curl_4.3.2
## [15] compiler_4.1.1 bookdown_0.24
## [17] sass_0.4.0 rappdirs_0.3.3
## [19] stringr_1.4.0 digest_0.6.28
## [21] rmarkdown_2.11 XVector_0.34.0
## [23] pkgconfig_2.0.3 htmltools_0.5.2
## [25] fastmap_1.1.0 highr_0.9
## [27] rlang_0.4.12 RSQLite_2.2.8
## [29] shiny_1.7.1 jquerylib_0.1.4
## [31] generics_0.1.1 jsonlite_1.7.2
## [33] dplyr_1.0.7 RCurl_1.98-1.5
## [35] magrittr_2.0.1 GenomeInfoDbData_1.2.7
## [37] Rcpp_1.0.7 Rhdf5lib_1.16.0
## [39] fansi_0.5.0 lifecycle_1.0.1
## [41] stringi_1.7.5 yaml_2.2.1
## [43] zlibbioc_1.40.0 grid_4.1.1
## [45] blob_1.2.2 parallel_4.1.1
## [47] promises_1.2.0.1 crayon_1.4.1
## [49] lattice_0.20-45 Biostrings_2.62.0
## [51] KEGGREST_1.34.0 magick_2.7.3
## [53] pillar_1.6.4 glue_1.4.2
## [55] BiocVersion_3.14.0 evaluate_0.14
## [57] BiocManager_1.30.16 vctrs_0.3.8
## [59] png_0.1-7 httpuv_1.6.3
## [61] purrr_0.3.4 assertthat_0.2.1
## [63] cachem_1.0.6 xfun_0.27
## [65] mime_0.12 xtable_1.8-4
## [67] later_1.3.0 tibble_3.1.5
## [69] snow_0.4-4 AnnotationDbi_1.56.1
## [71] memoise_2.0.0 ellipsis_0.3.2
## [73] interactiveDisplayBase_1.32.0