README.md
In CellBench: Construct Benchmarks for Single Cell Analysis Methods

CellBench

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

Installation

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.

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.

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

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