beachmat 1.2.1
beachmat has a few useful utilities outside of the C++ API. This document describes how to use them.
Given the dimensions of a matrix, users can choose HDF5 chunk dimensions that give fast performance for both row- and column-level access.
library(beachmat)
nrows <- 10000
ncols <- 200
getBestChunkDims(c(nrows, ncols))
## [1] 708 15
In the future, it should be possible to feed this back into the API.
Currently, if chunk dimensions are not specified in the C++ code, the API will retrieve them from R via the getHDF5DumpChunkDim()
function from HDF5Array.
The aim is to also provide a setHDF5DumpChunkDim()
function so that any chunk dimension specified in R will be respected.
The most common access patterns for matrices (at least, for high-throughput biological data) is by row or by column.
The rechunkByMargins()
will take a HDF5 file and convert it to using purely row- or column-based chunks.
library(HDF5Array)
A <- as(matrix(runif(5000), nrow=100, ncol=50), "HDF5Array")
byrow <- rechunkByMargins(A, byrow=TRUE)
byrow
## <100 x 50> HDF5Matrix object of type "double":
## [,1] [,2] [,3] ... [,49] [,50]
## [1,] 0.56255186 0.83748576 0.59590163 . 0.8206885 0.8822453
## [2,] 0.15557033 0.56658438 0.62395089 . 0.4702967 0.1473072
## [3,] 0.05805285 0.99513841 0.99089764 . 0.9474082 0.9386334
## [4,] 0.12489074 0.99634430 0.79680600 . 0.3950245 0.8057325
## [5,] 0.46060778 0.32031363 0.50575895 . 0.2542922 0.7555907
## ... . . . . . .
## [96,] 0.153485853 0.529738530 0.008270724 . 0.77627432 0.05214570
## [97,] 0.097863280 0.403424662 0.862630078 . 0.19495715 0.53035455
## [98,] 0.970400211 0.572643731 0.463074813 . 0.19958239 0.27946596
## [99,] 0.329732096 0.666838235 0.423838963 . 0.92683161 0.05196235
## [100,] 0.871021765 0.455680494 0.913476148 . 0.43593475 0.15573580
bycol <- rechunkByMargins(A, byrow=FALSE)
bycol
## <100 x 50> HDF5Matrix object of type "double":
## [,1] [,2] [,3] ... [,49] [,50]
## [1,] 0.56255186 0.83748576 0.59590163 . 0.8206885 0.8822453
## [2,] 0.15557033 0.56658438 0.62395089 . 0.4702967 0.1473072
## [3,] 0.05805285 0.99513841 0.99089764 . 0.9474082 0.9386334
## [4,] 0.12489074 0.99634430 0.79680600 . 0.3950245 0.8057325
## [5,] 0.46060778 0.32031363 0.50575895 . 0.2542922 0.7555907
## ... . . . . . .
## [96,] 0.153485853 0.529738530 0.008270724 . 0.77627432 0.05214570
## [97,] 0.097863280 0.403424662 0.862630078 . 0.19495715 0.53035455
## [98,] 0.970400211 0.572643731 0.463074813 . 0.19958239 0.27946596
## [99,] 0.329732096 0.666838235 0.423838963 . 0.92683161 0.05196235
## [100,] 0.871021765 0.455680494 0.913476148 . 0.43593475 0.15573580
Rechunking can provide a substantial speed-up to downstream functions, especially those requiring access to random columns or rows.
Indeed, the time saved in those functions often offsets the time spent in constructing a new HDF5Matrix
.
sessionInfo()
## R version 3.5.0 (2018-04-23)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: OS X El Capitan 10.11.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] HDF5Array_1.8.0 rhdf5_2.24.0 DelayedArray_0.6.0
## [4] BiocParallel_1.14.1 IRanges_2.14.10 S4Vectors_0.18.2
## [7] BiocGenerics_0.26.0 matrixStats_0.53.1 beachmat_1.2.1
## [10] knitr_1.20 BiocStyle_2.8.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.17 magrittr_1.5 stringr_1.3.1 tools_3.5.0
## [5] xfun_0.1 htmltools_0.3.6 yaml_2.1.19 rprojroot_1.3-2
## [9] digest_0.6.15 bookdown_0.7 Rhdf5lib_1.2.1 evaluate_0.10.1
## [13] rmarkdown_1.9 stringi_1.2.2 compiler_3.5.0 backports_1.1.2