To install from Bioconductor, use the following code:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("BPCellsArray")You can install the development version of BPCellsArray from
GitHub with:
if (!requireNamespace("pak")) {
install.packages("pak",
repos = sprintf(
"https://r-lib.github.io/p/pak/devel/%s/%s/%s",
.Platform$pkgType, R.Version()$os, R.Version()$arch
)
)
}
pak::pkg_install("Yunuuuu/BPCellsArray@main")BPCells is a package for high performance single cell analysis on RNA-seq and ATAC-seq datasets. This package just bring BPCells into Bioconductor single-cell workflow.
Almost all operations in BPCells are lazy, which means that no real
work is performed on BPCellsMatrix objects until the result needs to
be returned as an R object or written to disk. And most operations have
been optimized by c++ or c. Although DelayedArray package provides
block processing for most usual operations, BPCellsArray re-dispatch
these methods to use the optimized methods in BPCells.
Here is a summarized delayed operations in BPCells:
| Operations | BPCells | BPCellsArray |
|---|---|---|
| Combine by row | rbind2 | rbind2,rbind,arbind,bindROWS |
| Combine by column | cbind2 | cbind2,cbind,acbind,bindCOLS |
| transpose matrix | t | t |
| subset | [ |
[ |
| Rename | dimnames<- |
dimnames<-,rownames<-,colnames<- |
| Multiplication | %*% |
%*% |
| Crossproduct | crossprod | |
| Matrix product transpose | tcrossprod | |
| Arithmetic | +,-,*,/ |
+,-,*,/ |
| Relational Operators | Binary (<,>,<=, >=) |
Binary (<,>,<=, >=) |
| Storage mode | convert_matrix_type | convert_mode |
| Rank-transform | rank_transform | rank_transform,rowRanks,colRanks |
| Mask matrix entries to zero | mask_matrix | mask_matrix |
| Take minumum with a global constant | min_scalar | pmin_scalar |
| Take the minimum with a per-col constant | min_by_col | pmin_by_col |
| Take the minimum with a per-row constant | min_by_row | pmin_by_row |
| Round number | round | round |
exp(x) - 1 |
expm1_slow,expm1 |
expm1_slow,expm1 |
log(1+x) |
log1p,log1p_slow |
log1p_single,log1p |
| Power | pow_slow,^ |
pow_slow,^ |
Other non-lazied operations:
| Operations | BPCells | BPCellsArray | Note |
|---|---|---|---|
| row/col summarize | matrix_stats | matrix_stats | |
| row summarize | rowSums,rowMeans | rowSums,rowMeans,rowVars,rowSds | |
| col summarize | colSums,colMeans | colSums,colMeans,colVars,colSds | |
| Multiplication | %*% | %*% | For some methods |
| Crossproduct | crossprod | For some methods | |
| Matrix product transpose | tcrossprod | For some methods | |
| svd | svds | runSVD+SpectraParam |
BPCells provide following formats:
- Directory of files
- read:
readBPCellsDirMatrix - write:
writeBPCellsDirMatrix
- read:
- HDF5 file
- read:
readBPCellsHDF5Matrix - write:
writeBPCellsHDF5Matrix
- read:
- 10x HDF5 file
- read:
readBPCells10xHDF5Matrix - write:
writeBPCells10xHDF5Matrix
- read:
- in memory
- write:
writeBPCellsMemMatrix
- write:
Matrices can be stored in a directory on disk, in memory, or in an HDF5 file. Saving in a directory on disk is a good default for local analysis, as it provides the best I/O performance and lowest memory usage. The HDF5 format allows saving within existing hdf5 files to group data together, and the in memory format provides the fastest performance in the event memory usage is unimportant.
Details see: https://bnprks.github.io/BPCells/articles/web-only/bitpacking-format.html
library(BPCellsArray)
library(SingleCellExperiment)
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#>
#> Attaching package: 'matrixStats'
#> The following objects are masked from 'package:BPCellsArray':
#>
#> colRanks, colSds, colVars, rowRanks, rowSds, rowVars
#>
#> Attaching package: 'MatrixGenerics'
#> The following objects are masked from 'package:matrixStats':
#>
#> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#> colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#> colWeightedMeans, colWeightedMedians, colWeightedSds,
#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#> rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#>
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:stats':
#>
#> IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#>
#> anyDuplicated, aperm, append, as.data.frame, basename, cbind,
#> colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
#> get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
#> match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#> Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
#> table, tapply, union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#>
#> Attaching package: 'S4Vectors'
#> The following object is masked from 'package:utils':
#>
#> findMatches
#> The following objects are masked from 'package:base':
#>
#> expand.grid, I, unname
#> Loading required package: IRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: Biobase
#> Welcome to Bioconductor
#>
#> Vignettes contain introductory material; view with
#> 'browseVignettes()'. To cite Bioconductor, see
#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
#>
#> Attaching package: 'Biobase'
#> The following object is masked from 'package:MatrixGenerics':
#>
#> rowMedians
#> The following objects are masked from 'package:matrixStats':
#>
#> anyMissing, rowMediansLet’s prepare some data for analysis
set.seed(1L)
path <- tempfile("BPCells")
sce <- scuttle::mockSCE(2000L, 3000L)
format(object.size(assay(sce, "counts")), "MB")
#> [1] "46.1 Mb"What we need to do is transform the counts matrix into a BPCellsMatrix
object.
counts_mat <- assay(sce, "counts")
bitpacking_mat <- writeBPCellsDirMatrix(counts_mat, path = path)
#> 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.
format(object.size(bitpacking_mat), "MB")
#> [1] "0.3 Mb"The path store the data can be obtained by path function.
identical(path(bitpacking_mat), path)
#> [1] TRUEWe can inspect the assay info by print it. Attention the class name.
bitpacking_mat
#> <3000 x 2000> sparse BPCellsMatrix object of type "double":
#> Cell_001 Cell_002 Cell_003 ... Cell_1999 Cell_2000
#> Gene_0001 0 0 0 . 0 0
#> Gene_0002 188 61 0 . 44 195
#> Gene_0003 625 897 1324 . 134 575
#> Gene_0004 0 0 0 . 0 0
#> Gene_0005 0 2 1 . 41 0
#> ... . . . . . .
#> Gene_2996 246 75 205 . 4 95
#> Gene_2997 0 89 120 . 28 0
#> Gene_2998 46 868 234 . 134 200
#> Gene_2999 217 1774 369 . 173 1415
#> Gene_3000 3014 215 1219 . 137 300
#>
#> Seed form: DelayedArray
#> Storage Data type: double
#> Storage axis: col major
#>
#> Queued Operations:
#> 3000x2000 double, sparse: [seed] Load compressed matrix from directoryYou can coerce it into a dense matrix or dgCMatrix to get the actual
value.
bitpacking_mat[1:10, 1:10]
#> <10 x 10> sparse BPCellsMatrix object of type "double":
#> Cell_001 Cell_002 Cell_003 ... Cell_009 Cell_010
#> Gene_0001 0 0 0 . 0 0
#> Gene_0002 188 61 0 . 20 107
#> Gene_0003 625 897 1324 . 349 433
#> Gene_0004 0 0 0 . 0 0
#> Gene_0005 0 2 1 . 1 34
#> Gene_0006 0 2 40 . 0 49
#> Gene_0007 0 7 0 . 0 56
#> Gene_0008 198 78 21 . 146 74
#> Gene_0009 464 494 21 . 135 167
#> Gene_0010 52 142 1 . 454 157
#>
#> Seed form: DelayedArray
#> Storage Data type: double
#> Storage axis: col major
#>
#> Queued Operations:
#> 10x10 double, sparse: Subset matrix
#> └─ 3000x2000 double, sparse: [seed] MatrixDir object
as.matrix(bitpacking_mat[1:10, 1:10])
#> Cell_001 Cell_002 Cell_003 Cell_004 Cell_005 Cell_006 Cell_007
#> Gene_0001 0 0 0 0 51 0 16
#> Gene_0002 188 61 0 2 15 33 129
#> Gene_0003 625 897 1324 289 1374 260 611
#> Gene_0004 0 0 0 0 0 0 0
#> Gene_0005 0 2 1 8 0 9 0
#> Gene_0006 0 2 40 79 1 0 0
#> Gene_0007 0 7 0 0 0 0 176
#> Gene_0008 198 78 21 24 51 52 506
#> Gene_0009 464 494 21 91 697 695 677
#> Gene_0010 52 142 1 14 8 353 58
#> Cell_008 Cell_009 Cell_010
#> Gene_0001 0 0 0
#> Gene_0002 53 20 107
#> Gene_0003 70 349 433
#> Gene_0004 9 0 0
#> Gene_0005 5 1 34
#> Gene_0006 1128 0 49
#> Gene_0007 0 0 56
#> Gene_0008 244 146 74
#> Gene_0009 1578 135 167
#> Gene_0010 335 454 157
as(bitpacking_mat[1:10, 1:10], "dgCMatrix")
#> 10 x 10 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 10 column names 'Cell_001', 'Cell_002', 'Cell_003' ... ]]
#>
#> Gene_0001 . . . . 51 . 16 . . .
#> Gene_0002 188 61 . 2 15 33 129 53 20 107
#> Gene_0003 625 897 1324 289 1374 260 611 70 349 433
#> Gene_0004 . . . . . . . 9 . .
#> Gene_0005 . 2 1 8 . 9 . 5 1 34
#> Gene_0006 . 2 40 79 1 . . 1128 . 49
#> Gene_0007 . 7 . . . . 176 . . 56
#> Gene_0008 198 78 21 24 51 52 506 244 146 74
#> Gene_0009 464 494 21 91 697 695 677 1578 135 167
#> Gene_0010 52 142 1 14 8 353 58 335 454 157All DelayedArray methods can be used, especially, the block-processing
statistical methods. You can check
DelayedMatrixStats
pacakge for more supported matrix statisticals.
identical(rowMins(bitpacking_mat), rowMins(counts_mat, useNames = TRUE))
#> [1] TRUE
identical(rowMaxs(bitpacking_mat), rowMaxs(counts_mat, useNames = TRUE))
#> [1] TRUEAgain, no real work is performed on the matrix until the result needs to
be returned as an R object or written to disk. Attention the Queued Operations information.
assay(sce, "counts") <- bitpacking_mat
sce <- scuttle::logNormCounts(sce)
assay(sce, "logcounts")
#> <3000 x 2000> sparse BPCellsMatrix object of type "double":
#> Cell_001 Cell_002 Cell_003 ... Cell_1999 Cell_2000
#> Gene_0001 0.0000000 0.0000000 0.0000000 . 0.000000 0.000000
#> Gene_0002 7.5384602 6.0269795 0.0000000 . 5.533738 7.616844
#> Gene_0003 9.2661476 9.8844377 10.3411101 . 7.119326 9.172066
#> Gene_0004 0.0000000 0.0000000 0.0000000 . 0.000000 0.000000
#> Gene_0005 0.0000000 1.6346787 0.9847366 . 5.434136 0.000000
#> ... . . . . . .
#> Gene_2996 7.924555 6.320925 7.655961 . 2.356288 6.587085
#> Gene_2997 0.000000 6.564998 6.888430 . 4.899349 0.000000
#> Gene_2998 5.531192 9.837076 7.845959 . 7.119326 7.653186
#> Gene_2999 7.744385 10.867509 8.500775 . 7.485524 10.469749
#> Gene_3000 11.534041 7.828500 10.222001 . 7.151042 8.235757
#>
#> Seed form: DelayedArray
#> Storage Data type: double
#> Storage axis: col major
#>
#> Queued Operations:
#> 3000x2000 double, sparse: Transform by scale and (or) shift
#> └─ 3000x2000 double, sparse: Transform by `log1p` (double-precision)
#> └─ 3000x2000 double, sparse: Transform by scale and (or) shift
#> └─ 3000x2000 double, sparse: [seed] MatrixDir objectBoth count and logcounts share the same disk path.
identical(path(assay(sce, "counts")), path(assay(sce, "logcounts")))
#> [1] TRUEdec_sce <- scran::modelGeneVar(sce)
set.seed(1L)
scater::runPCA(sce,
subset_row = scran::getTopHVGs(dec_sce, n = 2000L),
BSPARAM = BiocSingular::IrlbaParam()
)
#> class: SingleCellExperiment
#> dim: 3000 2000
#> metadata(0):
#> assays(2): counts logcounts
#> rownames(3000): Gene_0001 Gene_0002 ... Gene_2999 Gene_3000
#> rowData names(0):
#> colnames(2000): Cell_001 Cell_002 ... Cell_1999 Cell_2000
#> colData names(4): Mutation_Status Cell_Cycle Treatment sizeFactor
#> reducedDimNames(1): PCA
#> mainExpName: NULL
#> altExpNames(1): SpikesBPCells has implement a C++ Spectra solver for SVD calculation,
BPCellsArray has wrap it into SpectraParam, the same format with
other BiocSingular function.
set.seed(1L)
sce <- scater::runPCA(sce,
subset_row = scran::getTopHVGs(dec_sce, n = 2000L),
BSPARAM = SpectraParam()
)
colLabels(sce) <- scran::clusterCells(
sce,
use.dimred = "PCA",
BLUSPARAM = bluster::SNNGraphParam(
k = 20L, type = "jaccard",
cluster.fun = "leiden",
cluster.args = list(
objective_function = "modularity",
resolution_parameter = 1,
n_iterations = -1L # undocumented characteristics
),
BNPARAM = BiocNeighbors::AnnoyParam()
)
)
sce <- scater::runUMAP(
sce,
dimred = "PCA",
n_neighbors = 10L,
min_dist = 0.3,
metric = "cosine",
external_neighbors = TRUE,
BNPARAM = BiocNeighbors::AnnoyParam()
)
scater::plotReducedDim(
sce, "UMAP",
colour_by = "label",
point_shape = 16,
point.padding = 0,
force = 0
)
sessionInfo()
#> R version 4.3.1 (2023-06-16)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 LTS
#>
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libmkl_rt.so; LAPACK version 3.8.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [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: Asia/Shanghai
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] SingleCellExperiment_1.22.0 SummarizedExperiment_1.30.2
#> [3] Biobase_2.60.0 GenomicRanges_1.52.0
#> [5] GenomeInfoDb_1.36.1 IRanges_2.34.1
#> [7] S4Vectors_0.38.1 BiocGenerics_0.46.0
#> [9] MatrixGenerics_1.12.3 matrixStats_1.2.0
#> [11] BPCellsArray_0.0.0.9000
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.0 viridisLite_0.4.2
#> [3] farver_2.1.1 dplyr_1.1.4
#> [5] vipor_0.4.5 viridis_0.6.4
#> [7] bitops_1.0-7 fastmap_1.1.1
#> [9] RCurl_1.98-1.12 bluster_1.11.4
#> [11] digest_0.6.33 rsvd_1.0.5
#> [13] lifecycle_1.0.4 cluster_2.1.4
#> [15] statmod_1.5.0 magrittr_2.0.3
#> [17] compiler_4.3.1 rlang_1.1.2
#> [19] tools_4.3.1 igraph_1.5.0.1
#> [21] utf8_1.2.4 yaml_2.3.8
#> [23] knitr_1.45 labeling_0.4.3
#> [25] S4Arrays_1.3.2 dqrng_0.3.0
#> [27] DelayedArray_0.29.0 abind_1.4-5
#> [29] BiocParallel_1.34.2 withr_2.5.2
#> [31] grid_4.3.1 fansi_1.0.6
#> [33] beachmat_2.16.0 colorspace_2.1-0
#> [35] edgeR_3.42.4 ggplot2_3.4.4
#> [37] scales_1.3.0 cli_3.6.2
#> [39] rmarkdown_2.25 crayon_1.5.2
#> [41] generics_0.1.3 metapod_1.8.0
#> [43] RSpectra_0.16-1 DelayedMatrixStats_1.22.1
#> [45] scuttle_1.10.1 ggbeeswarm_0.7.2
#> [47] zlibbioc_1.46.0 parallel_4.3.1
#> [49] XVector_0.40.0 BPCells_0.1.0
#> [51] vctrs_0.6.5 Matrix_1.6-4
#> [53] BiocSingular_1.16.0 BiocNeighbors_1.18.0
#> [55] ggrepel_0.9.3 irlba_2.3.5.1
#> [57] beeswarm_0.4.0 scater_1.31.1
#> [59] locfit_1.5-9.8 limma_3.56.2
#> [61] glue_1.6.2 codetools_0.2-19
#> [63] cowplot_1.1.1 uwot_0.1.16
#> [65] gtable_0.3.4 ScaledMatrix_1.8.1
#> [67] munsell_0.5.0 tibble_3.2.1
#> [69] pillar_1.9.0 htmltools_0.5.7
#> [71] GenomeInfoDbData_1.2.10 R6_2.5.1
#> [73] sparseMatrixStats_1.12.2 evaluate_0.23
#> [75] lattice_0.22-5 highr_0.10
#> [77] scran_1.28.2 Rcpp_1.0.11
#> [79] gridExtra_2.3 SparseArray_1.3.3
#> [81] xfun_0.41 pkgconfig_2.0.3
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