Last updated: 2025-04-18
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Knit directory: muse/
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Ignored: data/97516b79-8d08-46a6-b329-5d0a25b0be98.h5ad
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b082b56 | Dave Tang | 2025-04-18 | Exporting and loading |
| html | 48410a3 | Dave Tang | 2025-04-17 | Build site. |
| Rmd | 0a5c69f | Dave Tang | 2025-04-17 | Relative paths |
| html | 277808a | Dave Tang | 2025-04-17 | Build site. |
| Rmd | 2141310 | Dave Tang | 2025-04-17 | Modifying matrix path |
| html | aded5ff | Dave Tang | 2025-04-16 | Build site. |
| Rmd | 2a577d9 | Dave Tang | 2025-04-16 | Checking out the BPCells package |
BPCells is an R package that allows for computationally efficient single-cell analysis. It utilizes bit-packing compression to store counts matrices on disk and C++ code to cache operations.
remotes::install_github("bnprks/BPCells/r")Load packages.
suppressPackageStartupMessages(library(BPCells))
suppressPackageStartupMessages(library(Matrix))Write matrix to disk using BPCells.
set.seed(1984)
bpcells_dir <- 'bpcells_matrix'
if(dir.exists(bpcells_dir)){
unlink(bpcells_dir, recursive = TRUE)
}
write_matrix_dir(
mat = rsparsematrix(50000, 50000, density = 0.01),
dir = bpcells_dir
)Warning: Matrix compression performs poorly with non-integers.
• Consider calling convert_matrix_type if a compressed integer matrix is intended.
This message is displayed once every 8 hours.50000 x 50000 IterableMatrix object with class MatrixDir
Row names: unknown names
Col names: unknown names
Data type: double
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/bpcells_matrixOpen the BPCells matrix from disk.
bp_mat <- open_matrix_dir(bpcells_dir)
bp_mat50000 x 50000 IterableMatrix object with class MatrixDir
Row names: unknown names
Col names: unknown names
Data type: double
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/bpcells_matrixCalculate row and column sums (lazily, disk-backed).
row_sums <- rowSums(bp_mat)
col_sums <- colSums(bp_mat)
head(row_sums)[1] -3.04895 -15.53270 33.77594 -16.79850 -1.07540 12.19100dense_row <- as.matrix(bp_mat[1, ])Warning: Converting to a dense matrix may use excessive memory
This message is displayed once every 8 hours.sum(dense_row)[1] -3.04895Following the example by Ben Parks:
my_dir <- file.path(tempdir(), "data")
m1 <- matrix(1:12, nrow=3) |>
as("IterableMatrix") |>
write_matrix_dir(file.path(my_dir, "m1"), overwrite = TRUE) |>
log1p()
m13 x 4 IterableMatrix object with class TransformLog1p
Row names: unknown names
Col names: unknown names
Data type: double
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory /tmp/RtmpGBuknV/data/m1
2. Transform log1pall_matrix_inputs() strips away any queued operations,
i.e., Transform lop1p is gone. We can use
inputs to modify the path. Note that the queued operations
in m1 are intact.
inputs <- all_matrix_inputs(m1)
str(inputs)List of 1
$ :Formal class 'MatrixDir' [package "BPCells"] with 7 slots
.. ..@ dir : chr "/tmp/RtmpGBuknV/data/m1"
.. ..@ compressed : logi TRUE
.. ..@ buffer_size: int 8192
.. ..@ type : chr "double"
.. ..@ dim : int [1:2] 3 4
.. ..@ transpose : logi FALSE
.. ..@ dimnames :List of 2
.. .. ..$ : NULL
.. .. ..$ : NULLm13 x 4 IterableMatrix object with class TransformLog1p
Row names: unknown names
Col names: unknown names
Data type: double
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory /tmp/RtmpGBuknV/data/m1
2. Transform log1pCreate another matrix.
m2 <- matrix(1:21, nrow=3) |>
as("IterableMatrix") |>
write_matrix_dir(file.path(my_dir, "m2"), overwrite = TRUE)
m23 x 7 IterableMatrix object with class MatrixDir
Row names: unknown names
Col names: unknown names
Data type: double
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory /tmp/RtmpGBuknV/data/m2Modify path.
inputs[[1]]@dir <- file.path(my_dir, "m2")
all_matrix_inputs(m1) <- inputs
m13 x 4 IterableMatrix object with class TransformLog1p
Row names: unknown names
Col names: unknown names
Data type: double
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory /tmp/RtmpGBuknV/data/m2
2. Transform log1pCheck that it is using m2, which has 7 columns.
Matrix::colMeans(m1)[1] 1.059351 1.782369 2.193084 2.482584 2.706565 2.889341 3.043766It seems that write_matrix_dir() uses full paths by
default.
m3 <- matrix(1:12, nrow=3) |>
as("IterableMatrix") |>
write_matrix_dir("m3", overwrite = TRUE) |>
log1p()
m33 x 4 IterableMatrix object with class TransformLog1p
Row names: unknown names
Col names: unknown names
Data type: double
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/m3
2. Transform log1pWill it work if I modify it to a relative path?
m3_inputs <- all_matrix_inputs(m3)
m3_inputs[[1]]@dir <- file.path("m3")
all_matrix_inputs(m3) <- m3_inputs
m33 x 4 IterableMatrix object with class TransformLog1p
Row names: unknown names
Col names: unknown names
Data type: double
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory m3
2. Transform log1pCalculate column means.
Matrix::colMeans(m3)[1] 1.059351 1.782369 2.193084 2.482584Use saveRDS().
saveRDS(object = m3, file = paste0(my_dir, 'm3.rds'))Load.
m3_loaded <- readRDS(paste0(my_dir, 'm3.rds'))
m3_loaded3 x 4 IterableMatrix object with class TransformLog1p
Row names: unknown names
Col names: unknown names
Data type: double
Storage order: column major
Queued Operations:
1. Load compressed matrix from directory m3
2. Transform log1pFor m3 we used a relative path, so it will work if the
matrix directory exists in the current directory (which it should).
Matrix::colMeans(m3)[1] 1.059351 1.782369 2.193084 2.482584
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[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
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Matrix_1.7-0 BPCells_0.3.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] jsonlite_1.8.9 compiler_4.4.1 promises_1.3.2
[4] Rcpp_1.0.13 stringr_1.5.1 git2r_0.35.0
[7] GenomicRanges_1.58.0 callr_3.7.6 later_1.3.2
[10] jquerylib_0.1.4 IRanges_2.40.1 yaml_2.3.10
[13] fastmap_1.2.0 lattice_0.22-6 XVector_0.46.0
[16] R6_2.5.1 GenomeInfoDb_1.42.3 knitr_1.48
[19] BiocGenerics_0.52.0 tibble_3.2.1 MatrixGenerics_1.18.1
[22] rprojroot_2.0.4 GenomeInfoDbData_1.2.13 bslib_0.8.0
[25] pillar_1.10.1 rlang_1.1.4 cachem_1.1.0
[28] stringi_1.8.4 httpuv_1.6.15 xfun_0.48
[31] getPass_0.2-4 fs_1.6.4 sass_0.4.9
[34] cli_3.6.3 magrittr_2.0.3 zlibbioc_1.52.0
[37] ps_1.8.1 digest_0.6.37 grid_4.4.1
[40] processx_3.8.4 rstudioapi_0.17.1 lifecycle_1.0.4
[43] S4Vectors_0.44.0 vctrs_0.6.5 evaluate_1.0.1
[46] glue_1.8.0 whisker_0.4.1 stats4_4.4.1
[49] rmarkdown_2.28 httr_1.4.7 matrixStats_1.5.0
[52] UCSC.utils_1.2.0 tools_4.4.1 pkgconfig_2.0.3
[55] htmltools_0.5.8.1