Contents

1 Overview

beachmat has a few useful utilities outside of the C++ API. This document describes how to use them.

2 Choosing HDF5 chunk dimensions

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.

3 Rechunking a HDF5 file

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,]  0.1181245  0.8875337  0.3254774   . 0.70656007 0.39623515
##   [2,]  0.3498117  0.2388030  0.5047783   . 0.62947795 0.36708736
##   [3,]  0.6723204  0.9962194  0.6723537   . 0.78523727 0.91860941
##   [4,]  0.8487139  0.6463400  0.8024627   . 0.55188436 0.53735966
##   [5,]  0.7277063  0.2893102  0.8674756   . 0.77353529 0.01753654
##    ...          .          .          .   .          .          .
##  [96,] 0.68009318 0.41287443 0.57544743   .  0.9289865  0.2763149
##  [97,] 0.13603556 0.17802172 0.20724004   .  0.8014221  0.7448020
##  [98,] 0.49308121 0.92935112 0.42804175   .  0.4058182  0.1812442
##  [99,] 0.53140746 0.25712945 0.32461960   .  0.7158630  0.5151891
## [100,] 0.54541600 0.04084118 0.86567668   .  0.8634237  0.7218797
bycol <- rechunkByMargins(A, byrow=FALSE)
bycol
## <100 x 50> HDF5Matrix object of type "double":
##              [,1]       [,2]       [,3] ...      [,49]      [,50]
##   [1,]  0.1181245  0.8875337  0.3254774   . 0.70656007 0.39623515
##   [2,]  0.3498117  0.2388030  0.5047783   . 0.62947795 0.36708736
##   [3,]  0.6723204  0.9962194  0.6723537   . 0.78523727 0.91860941
##   [4,]  0.8487139  0.6463400  0.8024627   . 0.55188436 0.53735966
##   [5,]  0.7277063  0.2893102  0.8674756   . 0.77353529 0.01753654
##    ...          .          .          .   .          .          .
##  [96,] 0.68009318 0.41287443 0.57544743   .  0.9289865  0.2763149
##  [97,] 0.13603556 0.17802172 0.20724004   .  0.8014221  0.7448020
##  [98,] 0.49308121 0.92935112 0.42804175   .  0.4058182  0.1812442
##  [99,] 0.53140746 0.25712945 0.32461960   .  0.7158630  0.5151891
## [100,] 0.54541600 0.04084118 0.86567668   .  0.8634237  0.7218797

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.

4 Session information

sessionInfo()
## R version 3.5.1 Patched (2018-07-24 r75008)
## 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_1.10.0    rhdf5_2.26.0        DelayedArray_0.8.0 
##  [4] BiocParallel_1.16.0 IRanges_2.16.0      S4Vectors_0.20.0   
##  [7] BiocGenerics_0.28.0 matrixStats_0.54.0  beachmat_1.4.0     
## [10] knitr_1.20          BiocStyle_2.10.0   
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.19       magrittr_1.5       stringr_1.3.1     
##  [4] tools_3.5.1        xfun_0.4           htmltools_0.3.6   
##  [7] yaml_2.2.0         rprojroot_1.3-2    digest_0.6.18     
## [10] bookdown_0.7       Rhdf5lib_1.4.0     BiocManager_1.30.3
## [13] evaluate_0.12      rmarkdown_1.10     stringi_1.2.4     
## [16] compiler_3.5.1     backports_1.1.2