A guide to improving iscream’s runtime and memory efficiency
Running this vignette requires downloading 2GB of single-cell whole genome bisulfite sequencing (WGBS) BED files and tabix indices from this Zenodo record: https://zenodo.org/records/18089082.
library("BiocFileCache")
#> Loading required package: dbplyr
cachedir <- BiocFileCache()
snmc_zip_path <- bfcrpath(cachedir, "https://zenodo.org/records/18089082/files/sc_beds.zip")
snmc_unzip <- file.path(tempdir(), "sc_beds")
unzip(snmc_zip_path, exdir = snmc_unzip)
genes_file <- bfcrpath(cachedir, "https://zenodo.org/records/18089082/files/genes.bed")Select 100 human cell WGBS data from the snmC-seq2 (Luo et al. 2018) dataset:
snmc_dir <- file.path(snmc_unzip, "sc_biscuit")
bedfiles <- list.files(
snmc_dir,
pattern = "*.bed.gz$",
full.names = TRUE
)[1:100]Here we’ll be using 5000 gene body regions as the input:
library(data.table)
regions <- fread(
genes_file,
col.names = c("chr", "start", "end", "gene")
)[1:5000]
head(regions)
#> chr start end gene
#> <char> <int> <int> <char>
#> 1: chr1 1471764 1497848 ATAD3B
#> 2: chr1 3069167 3438621 PRDM16
#> 3: chr1 2403963 2413797 PEX10
#> 4: chr1 10472287 10630758 PEX14
#> 5: chr1 2425979 2505532 PLCH2
#> 6: chr1 9292893 9369532 SPSB1iscream uses multithreading to reduce querying runtime. The number of threads
can be set before or after loading the library. iscream will read the option
while loading and so can be set in .Rprofile
options("iscream.threads" = 8)
library(iscream)
#> iscream using 8 threads based on 'options(iscream.threads)' but parallelly::availableCores() detects 16 possibly available threads. See `?set_threads` for information on multithreading before trying to use more.Queries are parallelized across the input BED files. While this reduces
runtime, it increases memory usage as data from multiple files are loaded into
memory at the same time. For more information on multithreading see
?set_threads().
tabix()tabix() queries regions from BED files much like the tabix shell command,
but supports multiple files and can query them in parallel. Although fast,
tabix() may seem unresponsive on large queries as parallel::mclapply()
doesn’t show progression, but a progress bar is in development.
iscream has two tabix() implementations, one using the command line tabix
executable and the other using the htslib API. The command line tool is faster
as it can stream to a file which can be read from R while the htslib API stores
the strings in memory and is slower. iscream will look for the tabix executable
on startup and will only fall back to the htslib API if the executable is not
found. See ?tabix details for more information.
qt <- system.time(
tbx_query <- tabix(bedfiles, regions, col.names = c("beta", "coverage"))
)
tbx_query
#> chr start end beta coverage file
#> <char> <int> <int> <num> <int> <char>
#> 1: chr1 923949 923950 0.000 1 bisc_SRR6911624
#> 2: chr1 923953 923954 0.000 1 bisc_SRR6911624
#> 3: chr1 923959 923960 0.000 1 bisc_SRR6911624
#> 4: chr1 923971 923972 0.000 1 bisc_SRR6911624
#> 5: chr1 923973 923974 0.000 1 bisc_SRR6911624
#> ---
#> 45733375: chr4 190179369 190179370 0.000 2 bisc_SRR6911723
#> 45733376: chr4 190179686 190179687 1.000 1 bisc_SRR6911723
#> 45733377: chr4 190179687 190179688 0.500 2 bisc_SRR6911723
#> 45733378: chr4 190179753 190179754 1.000 1 bisc_SRR6911723
#> 45733379: chr4 190179754 190179755 0.333 3 bisc_SRR6911723On 8 threads, retrieving 45,733,379 records across 100 files took 11.014 seconds.
summarize_regionsTo get a summary of the information of the gene bodies use summarize_regions,
providing the gene name column as the feature column:
qt <- system.time(
summary_query <- summarize_regions(
bedfiles,
regions,
columns = 4,
fun = c("sum", "mean", "range", "count"),
col_names = "beta",
feature_col = "gene"
)
)
#> [15:00:22.484494] [iscream::summarize_regions] [info] Summarizing 5000 regions from 100 bedfiles
#> [15:00:22.484628] [iscream::summarize_regions] [info] using sum, mean, range, count
#> [15:00:22.484635] [iscream::summarize_regions] [info] with columns 4 as beta
head(summary_query)
#> chr start end file feature beta.sum beta.mean
#> <char> <int> <int> <char> <char> <num> <num>
#> 1: chr1 1471764 1497848 bisc_SRR6911624 ATAD3B 85.000 0.8585859
#> 2: chr1 3069167 3438621 bisc_SRR6911624 PRDM16 723.500 0.5288743
#> 3: chr1 2403963 2413797 bisc_SRR6911624 PEX10 15.000 0.2419355
#> 4: chr1 10472287 10630758 bisc_SRR6911624 PEX14 198.000 0.7764706
#> 5: chr1 2425979 2505532 bisc_SRR6911624 PLCH2 184.333 0.7228745
#> 6: chr1 9292893 9369532 bisc_SRR6911624 SPSB1 64.000 0.8648649
#> beta.range count
#> <num> <num>
#> 1: 1 99
#> 2: 1 1368
#> 3: 1 62
#> 4: 1 255
#> 5: 1 255
#> 6: 1 74On 8 threads, summarizing the 45,733,379
records across all 100 files took 4.683 seconds.
As with tabix(), runtime reduces and memory usage increases with increasing
thread counts.
make_matThe make_mat() function queries and stores every genomic location within the
input regions across input files. Unlike summarize_regions() the output
matrix dimensions are unknown at runtime. Although usually quite fast, if the
number of records falling into the input regions are large and there are few
overlaps between files, this can take a long time. Here, gene bodies are large
and the final matrix can contain millions of CpG loci. Further, with sparse
data, chances are new loci are found in every file.
Preallocating the number of rows, however, can drastically reduce runtime. Since we got 45 million loci/CpGs from all the BED files with the tabix query above, we can expect approximately between 5 and 10 million unique loci as single-cell WGBS data has lower coverage than bulk. We already have the tabix query so we can get the unique CpG count here and use it to preallocate the matrix, reducing the number of matrix resizes. We’ll add 100,000 extra rows on top of the existing count to be safe since every avoided resize cuts off at least a couple seconds from the runtime. Making tabix queries can be a relatively quick way to approximate the CpG count of a dataset. If you haven’t done a tabix query of the full dataset, you can approximate how many CpGs to expect based on CpG counts in one file and the coverage of your WGBS method. Here we make a matrix of the beta-values in the 4th column:
suppressPackageStartupMessages(library("SummarizedExperiment"))
cpg.count <- tbx_query$start |> unique() |> length()
qt <- system.time(meth_mat <- make_mat_se(
bedfiles,
regions,
column = 4,
sparse = TRUE,
prealloc = cpg.count + 1e5
))
#> [15:00:32.589487] [iscream::query_all] [info] Querying 5000 regions from 100 bedfiles
#>
#> [15:01:12.454511] [iscream::query_all] [info] Creating metadata vectors
#> [15:01:12.911981] [iscream::query_all] [info] 7276107 loci found - 16250 extra rows allocated with 0 resizes
#> [15:01:17.720398] [iscream::query_all] [info] Creating sparse matrix
meth_mat
#> class: RangedSummarizedExperiment
#> dim: 7276107 100
#> metadata(0):
#> assays(1): value
#> rownames: NULL
#> rowData names(0):
#> colnames(100): bisc_SRR6911624 bisc_SRR6911625 ... bisc_SRR6911722
#> bisc_SRR6911723
#> colData names(0):Making this 7,276,107 x 100 matrix took 45.812 seconds.
sessionInfo()
#> R version 4.5.0 (2025-04-11)
#> Platform: x86_64-pc-linux-gnu
#> Running under: AlmaLinux 9.6 (Sage Margay)
#>
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.utf8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.utf8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] SummarizedExperiment_1.40.0 Biobase_2.70.0
#> [3] GenomicRanges_1.62.0 Seqinfo_1.0.0
#> [5] IRanges_2.44.0 S4Vectors_0.48.0
#> [7] BiocGenerics_0.56.0 generics_0.1.4
#> [9] MatrixGenerics_1.22.0 matrixStats_1.5.0
#> [11] iscream_1.1.6 data.table_1.17.8
#> [13] BiocFileCache_3.0.0 dbplyr_2.5.1
#>
#> loaded via a namespace (and not attached):
#> [1] rappdirs_0.3.3 SparseArray_1.10.1 RSQLite_2.4.3
#> [4] lattice_0.22-7 magrittr_2.0.4 evaluate_1.0.5
#> [7] grid_4.5.0 fastmap_1.2.0 blob_1.2.4
#> [10] Matrix_1.7-4 DBI_1.2.3 purrr_1.1.0
#> [13] pbapply_1.7-4 httr2_1.2.1 abind_1.4-8
#> [16] cli_3.6.5 rlang_1.1.6 XVector_0.50.0
#> [19] parallelly_1.45.1 bit64_4.6.0-1 DelayedArray_0.36.0
#> [22] withr_3.0.2 cachem_1.1.0 S4Arrays_1.10.0
#> [25] tools_4.5.0 parallel_4.5.0 memoise_2.0.1
#> [28] dplyr_1.1.4 filelock_1.0.3 curl_7.0.0
#> [31] vctrs_0.6.5 R6_2.6.1 lifecycle_1.0.4
#> [34] stringfish_0.17.0 bit_4.6.0 pkgconfig_2.0.3
#> [37] RcppParallel_5.1.11-1 pillar_1.11.1 glue_1.8.0
#> [40] Rcpp_1.1.0 xfun_0.54 tibble_3.3.0
#> [43] tidyselect_1.2.1 knitr_1.50 compiler_4.5.0Luo, Chongyuan, Angeline Rivkin, Jingtian Zhou, Justin P. Sandoval, Laurie Kurihara, Jacinta Lucero, Rosa Castanon, et al. 2018. “Robust Single-Cell DNA Methylome Profiling with snmC-seq2.” Nat Commun 9 (1): 3824. https://doi.org/10.1038/s41467-018-06355-2.