<!-- README.md is generated from README.Rmd. Please edit that file --> # sparseMatrixStats <!-- badges: start --> [![codecov](https://codecov.io/gh/const-ae/sparseMatrixStats/branch/master/graph/badge.svg)](https://codecov.io/gh/const-ae/sparseMatrixStats) <!-- badges: end --> The goal of `sparseMatrixStats` is to make the API of the [matrixStats](https://github.com/HenrikBengtsson/matrixStats) available for sparse matrices. ## Installation You can install the release version of *[sparseMatrixStats](https://bioconductor.org/packages/3.10/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") ``` ## Example ``` 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 #> 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::colMedians(big_sparse_mat), matrixStats=matrixStats::colMedians(big_mat), apply=apply(big_mat, 2, median) ) #> # 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 29.73µs 33.56µs 28949. 7.76KB 26.1 #> 2 matrixStats 2.19ms 2.33ms 426. 162.31KB 0 #> 3 apply 18.28ms 18.55ms 53.8 17.23MB 202. ``` As you can see `sparseMatrixStats` is ca. 60 times fast than `matrixStats`, which in turn is 7 times faster than the `apply()` version. # API The package is still work in progress. For example, it is still completely lacking any documentation. Most functions have already been optimized for `dgCMatrix` input. The following list gives an overview which already have. In particular the `colXXXDiff()` functions have not yet been implemented. | 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() | ✔ | ✔ | |