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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.
You can install the development version of microdiluteR from GitHub with:
# install.packages("devtools") # if not installed already
devtools::install_github("silvia-eckert/microdiluteR")You can load microdiluteR as follows:
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 0.341For 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 ppmLet’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.