DelayedMatrixStats

A port of the matrixStats API to work with DelayedMatrix objects from the DelayedArray package

https://github.com/petehaitch/delayedmatrixstats

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A port of the matrixStats API to work with DelayedMatrix objects from the DelayedArray package

README.Rmd

---
output: github_document
editor_options: 
  chunk_output_type: console
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# DelayedMatrixStats

**DelayedMatrixStats** is a port of the 
[**matrixStats**](https://CRAN.R-project.org/package=matrixStats) API to work 
with *DelayedMatrix* objects from the 
[**DelayedArray**](http://bioconductor.org/packages/DelayedArray/) package.

For a *DelayedMatrix*, `x`, the simplest way to apply a function, `f()`, from 
**matrixStats** is`matrixStats::f(as.matrix(x))`. However, this "*realizes*" 
`x` in memory as a *base::matrix*, which typically defeats the entire purpose 
of using a *DelayedMatrix* for storing the data.

The **DelayedArray** package already implements a clever strategy called 
"block-processing" for certain common "matrix stats" operations (e.g. 
`colSums()`, `rowSums()`). This is a good start, but not all of the 
**matrixStats** API is currently supported. Furthermore, certain operations can 
be optimized with additional information about `x`. I'll refer to these 
"seed-aware" implementations.

## Installation

You can install **DelayedMatrixStats** from Bioconductor with:

```{r gh-installation, eval = FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install("DelayedMatrixStats")
```

## Example

This example compares two ways of computing column sums of a *DelayedMatrix* 
object:

1. `DelayedMatrix::colSums()`: The 'block-processing strategy', implemented in the **DelayedArray** package. The block-processing strategy works for any *DelayedMatrix* object, regardless of the type of *seed*.
2. `DelayedMatrixStats::colSums2()`: The 'seed-aware' strategy, implemented in the 
**DelayedMatrixStats** package. The seed-aware implementation is optimized for both speed and memory but only for *DelayedMatrix* objects with certain types of *seed*.

```{r real_pkg_load, message = FALSE, echo = FALSE}
devtools::load_all()
library(sparseMatrixStats)
library(microbenchmark)
library(profmem)
```

```{r fake_pkg_load, message = FALSE, echo = TRUE, eval = FALSE}
library(DelayedMatrixStats)
library(sparseMatrixStats)
library(microbenchmark)
library(profmem)
```

```{r example, message = FALSE}
set.seed(666)

# Fast column sums of DelayedMatrix with matrix seed
dense_matrix <- DelayedArray(matrix(runif(20000 * 600), nrow = 20000,
                                    ncol = 600))
class(seed(dense_matrix))
dense_matrix
microbenchmark(DelayedArray::colSums(dense_matrix),
               DelayedMatrixStats::colSums2(dense_matrix),
               times = 10)
profmem::total(profmem::profmem(DelayedArray::colSums(dense_matrix)))
profmem::total(profmem::profmem(DelayedMatrixStats::colSums2(dense_matrix)))

# Fast, low-memory column sums of DelayedMatrix with sparse matrix seed
sparse_matrix <- seed(dense_matrix)
zero_idx <- sample(length(sparse_matrix), 0.6 * length(sparse_matrix))
sparse_matrix[zero_idx] <- 0
sparse_matrix <- DelayedArray(Matrix::Matrix(sparse_matrix, sparse = TRUE))
class(seed(sparse_matrix))
sparse_matrix
microbenchmark(DelayedArray::colSums(sparse_matrix),
               DelayedMatrixStats::colSums2(sparse_matrix),
               times = 10)
profmem::total(profmem::profmem(DelayedArray::colSums(sparse_matrix)))
profmem::total(profmem::profmem(DelayedMatrixStats::colSums2(sparse_matrix)))

# Fast column sums of DelayedMatrix with Rle-based seed
rle_matrix <- RleArray(Rle(sample(2L, 200000 * 6 / 10, replace = TRUE), 100),
                       dim = c(2000000, 6))
class(seed(rle_matrix))
rle_matrix
microbenchmark(DelayedArray::colSums(rle_matrix),
               DelayedMatrixStats::colSums2(rle_matrix),
               times = 10)
profmem::total(profmem::profmem(DelayedArray::colSums(rle_matrix)))
profmem::total(profmem::profmem(DelayedMatrixStats::colSums2(rle_matrix)))
```

## Benchmarking

An extensive set of benchmarks is under development at [http://peterhickey.org/BenchmarkingDelayedMatrixStats/](http://peterhickey.org/BenchmarkingDelayedMatrixStats/).

## API coverage

- ✔ = Implemented in **DelayedMatrixStats**
- ☑️ = Implemented in [**DelayedArray**](http://bioconductor.org/packages/DelayedArray/) or [**sparseMatrixStats**](http://bioconductor.org/packages/sparseMatrixStats/)
- ❌: = Not yet implemented

```{r, echo = FALSE, comment="", results = "asis"}
matrixStats <- sort(
  c("colsum", "rowsum", grep("^(col|row)", 
                             getNamespaceExports("matrixStats"), 
                             value = TRUE)))
sparseMatrixStats <- getNamespaceExports("sparseMatrixStats")
DelayedMatrixStats <- getNamespaceExports("DelayedMatrixStats")
DelayedArray <- getNamespaceExports("DelayedArray")

api_df <- data.frame(
  Method = paste0("`", matrixStats, "()`"),
  `Block processing` = ifelse(
    matrixStats %in% DelayedMatrixStats,
    "✔",
    ifelse(matrixStats %in% c(DelayedArray, sparseMatrixStats), "☑️", "❌")),
  `_base::matrix_ optimized` = 
    ifelse(sapply(matrixStats, existsMethod, signature = "matrix_OR_array_OR_table_OR_numeric"), 
           "✔", 
           "❌"),
  `_Matrix::dgCMatrix_ optimized` = 
    ifelse(sapply(matrixStats, existsMethod, signature = "xgCMatrix") | sapply(matrixStats, existsMethod, signature = "dgCMatrix"), 
           "✔", 
           "❌"),
  `_Matrix::lgCMatrix_ optimized` = 
    ifelse(sapply(matrixStats, existsMethod, signature = "xgCMatrix") | sapply(matrixStats, existsMethod, signature = "lgCMatrix"), 
           "✔", 
           "❌"),
  `_DelayedArray::RleArray_ (_SolidRleArraySeed_) optimized` = 
    ifelse(sapply(matrixStats, existsMethod, signature = "SolidRleArraySeed"),
           "✔", 
           "❌"),
  `_DelayedArray::RleArray_  (_ChunkedRleArraySeed_) optimized` = 
    ifelse(sapply(matrixStats, existsMethod, signature = "ChunkedRleArraySeed"),
           "✔", 
           "❌"),
  `_HDF5Array::HDF5Matrix_ optimized` = 
    ifelse(sapply(matrixStats, existsMethod, signature = "HDF5ArraySeed"),
           "✔", 
           "❌"),
  `_base::data.frame_ optimized` = 
    ifelse(sapply(matrixStats, existsMethod, signature = "data.frame"),
           "✔", 
           "❌"),
  `_S4Vectors::DataFrame_ optimized` =
    ifelse(sapply(matrixStats, existsMethod, signature = "DataFrame"),
           "✔", 
           "❌"), 
  check.names = FALSE)
knitr::kable(api_df, row.names = FALSE)
```

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