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Fast and efficient statistical tools from the Tanay lab

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[![CRAN
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tgstat
======

The goal of `tgstat` is to provide fast and efficient implementation of
certain R functions such as ‘cor’ and ‘dist’, along with specific
statistical tools.

Various approaches are used to boost the performance, including
multi-processing and use of optimized functions provided by the Basic
Linear Algebra Subprograms (BLAS) library.

Installation
------------

Install from CRAN:

``` r
install.packages("tgstat")
```

For the development version:

``` r
remotes::install_github("tanaylab/tgstat")
```

Examples
--------

``` r
library(tgstat)
set.seed(seed = 60427)
rows <- 3000
cols <- 3000
vals <- sample(1:(rows * cols / 2), rows * cols, replace = T)
m <- matrix(vals, nrow = rows, ncol = cols)
m_with_NAs <- m
m_with_NAs[sample(1:(rows * cols), rows * cols / 10)] <- NA
dim(m)
#> [1] 3000 3000
```

### Fast computation of correlation matrices

Pearson correlation without BLAS, no NAs:

``` r
options(tgs_use.blas = F)
system.time(tgs_cor(m))
#>    user  system elapsed 
#> 106.865   1.951   2.331
```

Same with BLAS:

``` r
# tgs_cor, with BLAS, no NAs, pearson
options(tgs_use.blas = T)
system.time(tgs_cor(m))
#>    user  system elapsed 
#>   4.228   0.324   0.809
```

Base R version:

``` r
system.time(cor(m))
#>    user  system elapsed 
#>  21.780   0.078  21.857
```

Pearson correlation without BLAS, with NAs:

``` r
options(tgs_use.blas = F)
system.time(tgs_cor(m_with_NAs, pairwise.complete.obs = T))
#>    user  system elapsed 
#> 158.846   2.687   3.164
```

Same with BLAS:

``` r
options(tgs_use.blas = T)
system.time(tgs_cor(m_with_NAs, pairwise.complete.obs = T))
#>    user  system elapsed 
#>  11.286   1.173   0.803
```

Base R version:

``` r
system.time(cor(m_with_NAs, use = "pairwise.complete.obs"))
#>    user  system elapsed 
#> 311.627   0.182 311.823
```

### Fast computation of distance matrices

Distance without BLAS, no NAs:

``` r
options(tgs_use.blas = F)
system.time(tgs_dist(m))
#>    user  system elapsed 
#> 354.742   2.509   5.002
```

Same with BLAS:

``` r
options(tgs_use.blas = T)
system.time(tgs_dist(m))
#>    user  system elapsed 
#>   7.407   0.656   0.462
```

Base R:

``` r
system.time(dist(m, method = "euclidean"))
#>    user  system elapsed 
#> 164.197   0.077 164.280
```

Notes regarding the usage of `BLAS`
-----------------------------------

`tgstat` runs best when R is linked with an optimized BLAS
implementation.

Many optimized BLAS implementations are available, both proprietary
(e.g. Intel’s MKL, Apple’s vecLib) and opensource (e.g. OpenBLAS,
ATLAS). Unfortunately, R often uses by default the reference BLAS
implementation, which is known to have poor performance.

Having `tgstat` rely on the reference BLAS will result in very poor
performance and is strongly discouraged. If your R implementation uses
an optimized BLAS, set `options(tgs_use.blas=TRUE)` to allow `tgstat` to
make BLAS calls. Otherwise, set `options(tgs_use.blas=FALSE)` (default)
which instructs `tgstat` to avoid BLAS and instead rely only on its own
optimization methods. If in doubt, it is possible to run one of `tgstat`
CPU intensive functions (e.g. `tgs_cor`) and compare its run time under
both `options(tgs_use.blas=FALSE)`.

Exact instructions for linking R with an optimized BLAS library are
system dependent and are out of scope of this document.

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