CellBench

R package for benchmarking single cell analysis methods, primarily inspired by the modelling structure used in DSC.

Installation

r if (!require(remotes)) install.packages("remotes") remotes::install_github("shians/CellBench", ref = "R-3.5", build_opts = c("--no-resave-data", "--no-manual"))

Introduction

This package revolves around one object and one function. The benchmark_tbl (benchmark tibble) and the apply_methods(x, methods) function.

We expect data to to be stored in lists, and we apply functions stored in lists to the data. This creates a benchmark_tbl where the names of the lists items are stored as columns and the final column contains the result of the computations.

```r library(CellBench)

sample1 <- data.frame( x = matrix(runif(25), nrow = 5, ncol = 5) )

sample2 <- data.frame( x = matrix(runif(25), nrow = 5, ncol = 5) )

datasets <- list( sample1 = sample1, sample2 = sample2 )

transform <- list( correlation = cor, covariance = cov )

datasets %>% apply_methods(transform)

# A tibble: 4 x 3

data metric result

1 sample1 correlation

2 sample1 covariance

3 sample2 correlation

4 sample2 covariance

```

We can additionally chain method applications and this will combinatorially expand our benchmark_tbl so that combinations of methods can easily be computed.

```r metric <- list( mean = mean, median = median )

datasets %>% applymethods(transform) %>% applymethods(metric)

# A tibble: 8 x 4

data transform metric result

1 sample1 correlation mean 0.0602

2 sample1 correlation median -0.0520

3 sample1 covariance mean 0.00823

4 sample1 covariance median -0.00219

5 sample2 correlation mean 0.303

6 sample2 correlation median 0.482

7 sample2 covariance mean 0.0115

8 sample2 covariance median 0.0132

```

The result table is essentially a regular tibble and works with all tidyverse packages.

See

r vignette("Introduction", package = "CellBench")

for a more detailed introduction and example with biological data.

Features

• High compatibility with dplyr and rest of tidyverse, fundamental data object can be used with dplyr verbs
• Multithreading, methods can be applied in parallel

License

This package is licensed under GNU General Public License v3.0 (GPL-3.0).