The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

microdiluteR

GitHub R package version GitHub License R CMD check codecov GitHub commit activity Say Thanks

:notebook: 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.

:floppy_disk: Installation

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

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

:joystick: 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

:heart: Logo generated with hexSticker

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.