Name Mode Size
R 040000
data 040000
inst 040000
man 040000
tests 040000
vignettes 040000
.Rbuildignore 100644 0 kb
.covrignore 100644 0 kb
.gitignore 100644 0 kb
.travis.yml 100644 0 kb
CellBench.png 100644 170 kb
DESCRIPTION 100644 2 kb
LICENSE.md 100644 34 kb
NAMESPACE 100644 2 kb
NEWS.md 100644 2 kb
README.md 100644 3 kb
codecov.yml 100644 0 kb
README.md
# CellBench [![Travis build status](https://travis-ci.org/Shians/CellBench.svg?branch=master)](https://travis-ci.org/Shians/CellBench) [![Coverage status](https://codecov.io/gh/Shians/CellBench/branch/master/graph/badge.svg)](https://codecov.io/github/Shians/CellBench?branch=master) <img src="https://github.com/Shians/CellBench/raw/master/CellBench.png" width="150" /> R package for benchmarking single cell analysis methods, primarily inspired by the modelling structure used in [DSC](https://github.com/stephenslab/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](https://tibble.tidyverse.org)) 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 ## <fct> <fct> <list> ## 1 sample1 correlation <dbl [5 × 5]> ## 2 sample1 covariance <dbl [5 × 5]> ## 3 sample2 correlation <dbl [5 × 5]> ## 4 sample2 covariance <dbl [5 × 5]> ``` 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 %>% apply_methods(transform) %>% apply_methods(metric) ## # A tibble: 8 x 4 ## data transform metric result ## <fct> <fct> <fct> <dbl> ## 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).