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,] 0.8570540 0.2718039 0.3109512 . 0.38415832 0.44096151
## [2,] 0.7049978 0.5093208 0.5793587 . 0.67626533 0.63845107
## [3,] 0.1429905 0.1727937 0.6491899 . 0.17781961 0.78612775
## [4,] 0.9027074 0.9916206 0.8333865 . 0.04535817 0.55444957
## [5,] 0.1834593 0.7244919 0.7768454 . 0.03482194 0.99882906
## ... . . . . . .
## [96,] 0.1747163 0.1481265 0.3995648 . 0.4233303 0.5730586
## [97,] 0.2483783 0.5341786 0.8734796 . 0.6117993 0.4781657
## [98,] 0.5759660 0.1836422 0.1088920 . 0.8245431 0.5001515
## [99,] 0.1160643 0.5768999 0.2638522 . 0.3639379 0.4302823
## [100,] 0.9362426 0.6160199 0.6338973 . 0.7132248 0.6445599
bycol <- rechunkByMargins(A, byrow=FALSE)
bycol
## <100 x 50> HDF5Matrix object of type "double":
## [,1] [,2] [,3] ... [,49] [,50]
## [1,] 0.8570540 0.2718039 0.3109512 . 0.38415832 0.44096151
## [2,] 0.7049978 0.5093208 0.5793587 . 0.67626533 0.63845107
## [3,] 0.1429905 0.1727937 0.6491899 . 0.17781961 0.78612775
## [4,] 0.9027074 0.9916206 0.8333865 . 0.04535817 0.55444957
## [5,] 0.1834593 0.7244919 0.7768454 . 0.03482194 0.99882906
## ... . . . . . .
## [96,] 0.1747163 0.1481265 0.3995648 . 0.4233303 0.5730586
## [97,] 0.2483783 0.5341786 0.8734796 . 0.6117993 0.4781657
## [98,] 0.5759660 0.1836422 0.1088920 . 0.8245431 0.5001515
## [99,] 0.1160643 0.5768999 0.2638522 . 0.3639379 0.4302823
## [100,] 0.9362426 0.6160199 0.6338973 . 0.7132248 0.6445599
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 Patched (2018-05-03 r74699)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server 2012 R2 x64 (build 9600)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## 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