<!-- README.md is generated from README.Rmd. Please edit that file --> # sparseMatrixStats <a href='https://github.com/const-ae/sparseMatrixStats'><img src='man/figures/logo.png' align="right" height="209" /></a> <!-- badges: start --> [](https://codecov.io/gh/const-ae/sparseMatrixStats) <!-- badges: end --> The goal of `sparseMatrixStats` is to make the API of [matrixStats](https://github.com/HenrikBengtsson/matrixStats) available for sparse matrices. ## Installation You can install the release version of *[sparseMatrixStats](https://bioconductor.org/packages/sparseMatrixStats)* from BioConductor: ``` r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("sparseMatrixStats") ``` Alternatively, you can get the development version of the package from [GitHub](https://github.com/const-ae/sparseMatrixStats) with: ``` r # install.packages("devtools") devtools::install_github("const-ae/sparseMatrixStats") ``` If you have trouble with the installation, see the end of the README. ## Example ``` r library(sparseMatrixStats) #> Loading required package: MatrixGenerics #> Loading required package: matrixStats #> #> 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 ``` ``` r mat <- matrix(0, nrow=10, ncol=6) mat[sample(seq_len(60), 4)] <- 1:4 # Convert dense matrix to sparse matrix sparse_mat <- as(mat, "dgCMatrix") sparse_mat #> 10 x 6 sparse Matrix of class "dgCMatrix" #> #> [1,] 4 . . . . . #> [2,] . . . . . . #> [3,] . . . . . . #> [4,] 2 . . . . . #> [5,] . . . . . . #> [6,] . . . . . . #> [7,] . . . . . 1 #> [8,] . . . . . . #> [9,] . . . 3 . . #> [10,] . . . . . . ``` The package provides an interface to quickly do common operations on the rows or columns. For example calculate the variance: ``` r apply(mat, 2, var) #> [1] 1.822222 0.000000 0.000000 0.900000 0.000000 0.100000 matrixStats::colVars(mat) #> [1] 1.822222 0.000000 0.000000 0.900000 0.000000 0.100000 sparseMatrixStats::colVars(sparse_mat) #> [1] 1.822222 0.000000 0.000000 0.900000 0.000000 0.100000 ``` On this small example data, all methods are basically equally fast, but if we have a much larger dataset, the optimizations for the sparse data start to show. I generate a dataset with 10,000 rows and 50 columns that is 99% empty ``` r big_mat <- matrix(0, nrow=1e4, ncol=50) big_mat[sample(seq_len(1e4 * 50), 5000)] <- rnorm(5000) # Convert dense matrix to sparse matrix big_sparse_mat <- as(big_mat, "dgCMatrix") ``` I use the `bench` package to benchmark the performance difference: ``` r bench::mark( sparseMatrixStats=sparseMatrixStats::colVars(big_sparse_mat), matrixStats=matrixStats::colVars(big_mat), apply=apply(big_mat, 2, var) ) #> # A tibble: 3 x 6 #> expression min median `itr/sec` mem_alloc `gc/sec` #> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl> #> 1 sparseMatrixStats 37.3µs 42.71µs 20836. 2.93KB 14.6 #> 2 matrixStats 1.48ms 1.65ms 584. 156.8KB 2.03 #> 3 apply 10.61ms 11.18ms 88.9 9.54MB 48.2 ``` As you can see `sparseMatrixStats` is ca. 35 times fast than `matrixStats`, which in turn is 7 times faster than the `apply()` version. # API The package now supports all functions from the `matrixStats` API for column sparse matrices (`dgCMatrix`). And thanks to the [`MatrixGenerics`](https://bioconductor.org/packages/MatrixGenerics/) it can be easily integrated along-side [`matrixStats`](https://cran.r-project.org/package=matrixStats) and [`DelayedMatrixStats`](https://bioconductor.org/packages/DelayedMatrixStats/). Note that the `rowXXX()` functions are called by transposing the input and calling the corresponding `colXXX()` function. Special optimized implementations are available for `rowSums2()`, `rowMeans2()`, and `rowVars()`. | Method | matrixStats | sparseMatrixStats | Notes | | :------------------- | :---------- | :---------------- | :--------------------------------------------------------------------------------------- | | colAlls() | ✔ | ✔ | | | colAnyMissings() | ✔ | ❌ | Not implemented because it is deprecated in favor of `colAnyNAs()` | | 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() | ✔ | ✔ | | | colsum() | ✔ | ❌ | Base R function | | colSums2() | ✔ | ✔ | | | colTabulates() | ✔ | ✔ | | | colVarDiffs() | ✔ | ✔ | | | colVars() | ✔ | ✔ | | | colWeightedMads() | ✔ | ✔ | Sparse version behaves slightly differently, because it always uses `interpolate=FALSE`. | | colWeightedMeans() | ✔ | ✔ | | | colWeightedMedians() | ✔ | ✔ | Only equivalent if `interpolate=FALSE` | | colWeightedSds() | ✔ | ✔ | | | colWeightedVars() | ✔ | ✔ | | | rowAlls() | ✔ | ✔ | | | rowAnyMissings() | ✔ | ❌ | Not implemented because it is deprecated in favor of `rowAnyNAs()` | | 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() | ✔ | ✔ | | | rowsum() | ✔ | ❌ | Base R function | | rowSums2() | ✔ | ✔ | | | rowTabulates() | ✔ | ✔ | | | rowVarDiffs() | ✔ | ✔ | | | rowVars() | ✔ | ✔ | | | rowWeightedMads() | ✔ | ✔ | Sparse version behaves slightly differently, because it always uses `interpolate=FALSE`. | | rowWeightedMeans() | ✔ | ✔ | | | rowWeightedMedians() | ✔ | ✔ | Only equivalent if `interpolate=FALSE` | | rowWeightedSds() | ✔ | ✔ | | | rowWeightedVars() | ✔ | ✔ | | # Installation Problems `sparseMatrixStats` uses features from C++14 and as the standard is more than 6 years old, I thought this wouldn’t cause problems. In most circumstances this is true, but there are reoccuring reports, that the installation fails for some people and that is of course annoying. The typical error message is: Error: C++14 standard requested but CXX14 is not defined The main reason that the installation fails is that the compiler is too old. Sufficient support for C++14 came in - `clang` version 3.4 - `gcc` version 4.9 Accordingly, you must have a compiler available that is at least that new. If you run on the command line ``` bash $ gcc --version ``` and it says 4.8, you will have to install a newer compiler. At the end of the section, I have collected a few tips to install an appropriate version on different distributions. If you have recent version of `gcc` (\>=4.9) or `clang` (\>= 3.4) installed, but you still see the error message Error: C++14 standard requested but CXX14 is not defined the problem is that R doesn’t yet know about it. The solution is to either create a `~/.R/Makevars` file and define CXX14 = g++ CXX14FLAGS = -g -O2 $(LTO) CXX14PICFLAGS = -fpic CXX14STD = -std=gnu++14 or simply call ``` r withr::with_makevars( new = c(CXX14 = "g++", CXX14FLAGS = "-g -O2 $(LTO)", CXX14PICFLAGS = "-fpic", CXX14STD = "-std=gnu++14"), code = { BiocManager::install("sparseMatrixStats") }) ``` ### Update Compiler #### CentOS / Scientic Linux / RHEL One of the main culprits causing trouble is CentOS 7. It is popular in scientific computing and is still supported until 2024. It does, however, by default come with a very old version of `gcc` (4.8.5). To install a more recent compiler, we can use [devtoolset](https://www.softwarecollections.org/en/scls/rhscl/devtoolset-7/). First, we enable the Software Collection Tools and then install for example `gcc` version 7: ``` bash $ yum install centos-release-scl $ yum install devtoolset-7-gcc* ``` We can now either activate the new compiler for an R session ``` bash $ scl enable devtoolset-7 R ``` and then call ``` r withr::with_makevars( new = c(CXX14 = "g++", CXX14FLAGS = "-g -O2 $(LTO)", CXX14PICFLAGS = "-fpic", CXX14STD = "-std=gnu++14"), code = { BiocManager::install("sparseMatrixStats") }) ``` or we refer to the full path of the newly installed g++ from a standard R session ``` r withr::with_makevars( new = c(CXX14 = "/opt/rh/devtoolset-7/root/usr/bin/g++", CXX14FLAGS = "-g -O2 $(LTO)", CXX14PICFLAGS = "-fpic", CXX14STD = "-std=gnu++14"), code = { BiocManager::install("sparseMatrixStats") }) ``` Note, that our shenanigans are only necessary once, when we install `sparseMatrixStats`. After the successful installation of the package, we can use R as usual. #### Debian All Debian releases later than Jessie (i.e. Stretch, Buster, Bullseye) are recent enough and should install sparseMatrixStats without problems. I was able to install `sparseMatrixStats` on Debian Jessie (which comes with `gcc` version 4.9.2) by providing the necessary Makefile arguments ``` r withr::with_makevars( new = c(CXX14 = "g++", CXX14FLAGS = "-g -O2 $(LTO)", CXX14PICFLAGS = "-fpic", CXX14STD = "-std=gnu++14"), code = { BiocManager::install("sparseMatrixStats") }) ``` Debian Wheezy comes with `gcc` 4.7, which does not support C++14. On the other hand, the last R release that was backported to Wheezy is 3.2.5 (see information on [CRAN](https://cloud.r-project.org/bin/linux/debian/#debian-wheezy-oldoldoldstable)). Thus, if you are still on Wheezy, I would encourage you to update your OS. #### Ubuntu Since 16.04, Ubuntu comes with a recent enough compiler. Ubuntu 14.04 comes with `gcc` 4.8.5, but updating to `gcc-5` is easy: ``` bash $ sudo add-apt-repository ppa:ubuntu-toolchain-r/test $ sudo apt-get update $ sudo apt-get install gcc-5 g++-5 ``` After that, you can install `sparseMatrixStats` with a custom Makevars variables that refer to the new compiler ``` r withr::with_makevars( new = c(CXX14 = "g++-5", CXX14FLAGS = "-g -O2 $(LTO)", CXX14PICFLAGS = "-fpic", CXX14STD = "-std=gnu++14"), code = { BiocManager::install("sparseMatrixStats") }) ``` #### MacOS No trouble reported so far. Just do: ``` r BiocManager::install("sparseMatrixStats") ``` #### Windows It is important that you have [RTools40](https://cran.r-project.org/bin/windows/Rtools/) installed. After that, you shouldn’t have any troubles installing `sparseMatrixStats` directly from Bioconductor: ``` r BiocManager::install("sparseMatrixStats") ``` ### But I still have a problems 1. *Please* make sure to carefully read the full problem section. 2. Make sure that you are using at least R 4.0.0. 3. Make sure your compiler is new enough to support C++14 (ie. `gcc` \>= 4.9 and `clang` \>= 3.4) If your problems nonetheless persist, please file an [issue](https://github.com/const-ae/sparseMatrixStats/issues/) including the following information: - Operating system with exact version (e.g. ‘Linux Ubuntu 18.04’) - Compiler and compiler version (e.g. ‘gcc version 7.2.5’) - The output of `sessionInfo()` - Information if you have a `~/.R/Makevars` file and what it contains - The exact call that you use to install `sparseMatrixStats` including the full error message