microdiluteR

📓 Background

The microdiluteR package is designed to help researchers tidy up data from photometer plates and provides functions to easily add metadata, regardless of whether the user is processing a single plate or multiple plates with complex metadata structures. This package was developed with a special focus on the analysis of broth microdilution assays. A detailed tutorial can be found here.

📌 Citation

Creating this package was a lot of work, and I made it available for free. If you use this package for your publication, be fair and cite it, e.g. using the following biblatex entry:

@software(eckert_micodiluteR_2024,
  author  = {Eckert, Silvia},
  title   = {microdiluteR},
  version = {1.0.1.},
  date    = {2024-05-13}
  year    = {2024},
  note    = {R package version 1.0.1, available from CRAN},
  url     = {https://cran.r-project.org/package=microdiluteR}
  doi     = {10.5281/zenodo.11186926}
)

Or you can cite as follows using the APA citation style:

Eckert, S. (2024). microdiluteR (Version 1.0.1) [Software]. DOI: 10.5281/zenodo.11186926. Retrieved from: https://CRAN.R-project.org/package=microdiluteR

Or use citation("microdiluteR") to retrieve citation information after installing the R package.

💾 Installation

You can install the CRAN version of microdiluteR with:

install.packages("microdiluteR")

You can install the development version of microdiluteR from GitHub with:

# install.packages("devtools") # if not installed already
devtools::install_github("silvia-eckert/microdiluteR")

🕹️ Usage

You can load microdiluteR as follows:

library(microdiluteR)

Let’s try out the main function tidy_plates() with example data:

data(bma)
bma[1] # file name is bma_grp1_exp2_T0
#> $bma_grp1_exp2_T0
#>       1     2     3     4     5     6     7     8     9    10    11    12
#> A 0.342 0.354 0.360 0.360 0.352 0.363 0.361 0.352 0.356 0.351 0.366 0.375
#> B 0.362 0.391 0.375 0.363 0.383 0.366 0.380 0.378 0.339 0.387 0.377 0.362
#> C 0.344 0.346 0.345 0.347 0.350 0.356 0.348 0.343 0.348 0.351 0.351 0.353
#> D 0.361 0.367 0.351 0.364 0.353 0.362 0.361 0.367 0.363 0.356 0.357 0.355
#> E 0.388 0.473 0.400 0.358 0.388 0.340 0.335 0.396 0.411 0.404 0.397 0.407
#> F 0.456 0.465 0.469 0.469 0.462 0.468 0.455 0.477 0.487 0.488 0.498 0.471
#> G 0.334 0.340 0.357 0.332 0.329 0.342 0.333 0.317 0.360 0.332 0.335 0.328
#> H 0.334 0.332 0.339 0.333 0.339 0.334 0.342 0.335 0.361 0.327 0.330 0.341

For the example data, the corresponding metadata is stored as an attribute:

attr(bma, "metadata")
#>   plate_axis treatment concentration
#> 1          A       10%       100 ppm
#> 2          B       10%       200 ppm
#> 3          C       30%       100 ppm
#> 4          D       30%       200 ppm
#> 5          E      100%       100 ppm
#> 6          F      100%       200 ppm
#> 7          G   Control       100 ppm
#> 8          H   Control       200 ppm

Let’s add the metadata and create a tidy data frame for further processing:

tidy_data <- tidy_plates(bma[1],
                         how_many = "single",
                         direction = "horizontal",
                         validity_method = "threshold",
                         threshold = 0.355, # values above this are set as invalid
                         group_ID = "Group 1", # optional
                         experiment_name = "Experiment A", # optional
                         treatment_labels = rep(c("10%", "30%", "100%", "Control"), each = 2),
                         concentration_levels = rep(c(100,200), times = 4))

# Let's rename some columns for convenience
names(tidy_data)[names(tidy_data) == 'Position'] <- 'Pos'
names(tidy_data)[names(tidy_data) == 'Value'] <- 'Val'
names(tidy_data)[names(tidy_data) == 'Treatment'] <- 'Treat'
names(tidy_data)[names(tidy_data) == 'Concentration'] <- 'Conc'
names(tidy_data)[names(tidy_data) == 'Timepoint'] <- 'TP'

This is the resulting table:

tidy_data
#> # A tibble: 96 × 9
#>    Pos     Val Validity Treat  Conc TP    File             Group   Experiment  
#>    <chr> <dbl> <chr>    <chr> <dbl> <chr> <chr>            <chr>   <chr>       
#>  1 A-1   0.342 valid    10%     100 T0    bma_grp1_exp2_T0 Group 1 Experiment A
#>  2 A-2   0.354 valid    10%     100 T0    bma_grp1_exp2_T0 Group 1 Experiment A
#>  3 A-3   0.36  invalid  10%     100 T0    bma_grp1_exp2_T0 Group 1 Experiment A
#>  4 A-4   0.36  invalid  10%     100 T0    bma_grp1_exp2_T0 Group 1 Experiment A
#>  5 A-5   0.352 valid    10%     100 T0    bma_grp1_exp2_T0 Group 1 Experiment A
#>  6 A-6   0.363 invalid  10%     100 T0    bma_grp1_exp2_T0 Group 1 Experiment A
#>  7 A-7   0.361 invalid  10%     100 T0    bma_grp1_exp2_T0 Group 1 Experiment A
#>  8 A-8   0.352 valid    10%     100 T0    bma_grp1_exp2_T0 Group 1 Experiment A
#>  9 A-9   0.356 invalid  10%     100 T0    bma_grp1_exp2_T0 Group 1 Experiment A
#> 10 A-10  0.351 valid    10%     100 T0    bma_grp1_exp2_T0 Group 1 Experiment A
#> # ℹ 86 more rows

🛠️ Issues

If you find a 🪲 in the code, feel free to write this in the Issues section. If you want to contribute to this package or suggest a new feature, feel free to use the Pull Requests section.

📜 License

This repository and the files therein are licensed under the GPL-3.0 license.

---

❤️ Logo generated with hexSticker