---
output: github_document
---
<!-- README.md 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
<!-- badges: start -->
[](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 `r BiocStyle::Biocpkg("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 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::colMedians(big_sparse_mat),
matrixStats=matrixStats::colMedians(big_mat),
apply=apply(big_mat, 2, median)
)
```
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.
```{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)
```
