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--- output: github_document --- <!-- is generated from README.Rmd. Please edit that file --> ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" ) ``` # sparseMatrixStats <a href=''><img src='man/figures/logo.png' align="right" height="209" /></a> <!-- badges: start --> [![codecov](]( <!-- badges: end --> The goal of `sparseMatrixStats` is to make the API of [matrixStats]( available for sparse matrices. ## Installation You can install the release version of *[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]( with: ``` r # install.packages("devtools") devtools::install_github("const-ae/sparseMatrixStats") ``` ## Example ```{r include=FALSE} set.seed(1) ``` ```{r} library(sparseMatrixStats) ``` ```{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 ``` 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) matrixStats::colVars(mat) sparseMatrixStats::colVars(sparse_mat) ``` 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) ) ``` 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`]( it can be easily integrated along-side [`matrixStats`]( and [`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()`. ```{r, echo=FALSE, results="asis"} matrixStats_functions <- sort( c("colsum", "rowsum", grep("^(col|row)", getNamespaceExports("matrixStats"), value = TRUE))) DelayedMatrixStats_functions <- grep("^(col|row)", getNamespaceExports("DelayedMatrixStats"), value=TRUE) DelayedArray_functions <- grep("^(col|row)", getNamespaceExports("DelayedArray"), value=TRUE) sparseMatrixStats_functions <- grep("^(col|row)", getNamespaceExports("sparseMatrixStats"), value=TRUE) notes <- c("colAnyMissings"="Not implemented because it is deprecated in favor of `colAnyNAs()`", "rowAnyMissings"="Not implemented because it is deprecated in favor of `rowAnyNAs()`", "colsum"="Base R function", "rowsum"="Base R function", "colWeightedMedians"="Only equivalent if `interpolate=FALSE`", "rowWeightedMedians"="Only equivalent if `interpolate=FALSE`", "colWeightedMads"="Sparse version behaves slightly differently, because it always uses `interpolate=FALSE`.", "rowWeightedMads"="Sparse version behaves slightly differently, because it always uses `interpolate=FALSE`.") api_df <- data.frame( Method = paste0(matrixStats_functions, "()"), matrixStats = ifelse(matrixStats_functions %in% matrixStats_functions, "✔", "❌"), sparseMatrixStats = ifelse(matrixStats_functions %in%sparseMatrixStats_functions, "✔", "❌"), Notes = ifelse(matrixStats_functions %in% names(notes), notes[matrixStats_functions], ""), stringsAsFactors = FALSE ) knitr::kable(api_df, row.names = FALSE) ```