This function processes a single raw 96-well plate data from a photometer measurement by adding metadata via user-specified parameter values.
Arguments
- input_data
Either a file path or a data frames with 8 rows and 12 columns.
- direction
A character vector specifying the orientation of the plate layout. It can be either "horizontal" or "vertical".
- group_ID
A character vector providing group identifiers for each experiment.
- experiment_name
A string providing the name of the experiment. The hierarchy is group > experiment, i.e. within a single group, there might be several experiments taking place (e.g. multiple extracts from the same plant species tested with plant species being the group and type of extract being the experiment).
- validity_method
A character vector specifying the method for determining cell validity. It can be either "threshold" (i.e. samples are validated based on a common absorption maximum) or "samples" (i.e. samples are manually specified as invalid).
- threshold
A numeric threshold value. Applied if
validity_methodis set to 'threshold'.- invalid_samples
A character vector containing well positions (e.g. "A-3", "B-8",...) of invalid samples. Applied if
validity_methodis set to 'samples'.- treatment_labels
A character vector containing treatment labels.
- concentration_levels
A numeric vector containing concentration levels.
- ...
Additional arguments to be passed to
read_plates.
Details
This function processes photometer data from a single measurement and adds metadata based on user-set parameters.
It supports two methods for determining cell validity: "threshold" and "invalid". If "threshold" method is chosen,
the validity of each cell is determined based on a specified threshold value. If "sample" method is chosen, samples
at specified well positions on the plate are considered invalid. The function generates lists of treatments and
concentration levels based on the direction parameter, i.e. the direction of the treatments and concentration levels
applied (either horizontally or vertically on the plate). To add metadata to several plates at the same time, the
functions tidy_plates_via_params and tidy_plates_via_prompts are recommended.
For all three functions, tidy_plate, tidy_plates_via_params, and tidy_plates_via_prompts, to work
properly, file names should provide a file identifier (i.e. "bma" in
case there are additional but unused files in the folder), an identifier for experiments (starting with "exp" followed
by a number, e.g. "exp1") and an identifier for timepoints (starting with the upper- or lower-case letter t followed
by a number, e.g. "T0" or "t0").
Examples
# Load example data
data(bma)
# Add metadata from user parameters
bma_tidy <- tidy_single_plate(input_data = bma[1],
direction = "horizontal",
group_ID = "Group A",
experiment_name = "Experiment 1",
validity_method = "threshold",
threshold = 1,
treatment_labels = LETTERS[1:8],
concentration_levels = seq(from=80, to=10, length.out=8))
bma_tidy # View tidy data
#> # A tibble: 96 × 9
#> Position Value Validity Treatment Concentration Timepoint File Group
#> <chr> <dbl> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 A-1 0.342 valid A 80 T0 bma_grp1_exp… Grou…
#> 2 A-2 0.354 valid A 80 T0 bma_grp1_exp… Grou…
#> 3 A-3 0.36 valid A 80 T0 bma_grp1_exp… Grou…
#> 4 A-4 0.36 valid A 80 T0 bma_grp1_exp… Grou…
#> 5 A-5 0.352 valid A 80 T0 bma_grp1_exp… Grou…
#> 6 A-6 0.363 valid A 80 T0 bma_grp1_exp… Grou…
#> 7 A-7 0.361 valid A 80 T0 bma_grp1_exp… Grou…
#> 8 A-8 0.352 valid A 80 T0 bma_grp1_exp… Grou…
#> 9 A-9 0.356 valid A 80 T0 bma_grp1_exp… Grou…
#> 10 A-10 0.351 valid A 80 T0 bma_grp1_exp… Grou…
#> # ℹ 86 more rows
#> # ℹ 1 more variable: Experiment <chr>