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