Create a list of scoring functions (one per plate) that quantify the spatially homogeneous distribution of conditions across the plate

mk_plate_scoring_functions(
  batch_container,
  plate = NULL,
  row,
  column,
  group,
  p = 2,
  penalize_lines = "soft"
)

Arguments

batch_container: Batch container (bc) with all columns that denote plate related information
plate: Name of the bc column that holds the plate identifier (may be missing or NULL in case just one plate is used)
row: Name of the bc column that holds the plate row number (integer values starting at 1)
column: Name of the bc column that holds the plate column number (integer values starting at 1)
group: Name of the bc column that denotes a group/condition that should be distributed on the plate
p: p parameter for minkowski type of distance metrics. Special cases: p=1 - Manhattan distance; p=2 - Euclidean distance
penalize_lines: How to penalize samples of the same group in one row or column of the plate. Valid options are: 'none' - there is no penalty and the pure distance metric counts, 'soft' - penalty will depend on the well distance within the shared plate row or column, 'hard' - samples in the same row/column will score a zero distance

Value

List of scoring functions, one per plate, that calculate a real valued measure for the quality of the group distribution (the lower the better).

Examples

data("invivo_study_samples")
bc <- BatchContainer$new(
  dimensions = c("column" = 6, "row" = 10)
)
bc <- assign_random(bc, invivo_study_samples)
scoring_f <- mk_plate_scoring_functions(
  bc,
  row = "row", column = "column", group = "Sex"
)
bc <- optimize_design(bc, scoring = scoring_f, max_iter = 100)
#> Checking variances of 1-dim. score vector.
#> ... (0.15) - OK
#> Initial score: 9.413
#> Achieved score: 9.364 at iteration 5
#> Achieved score: 9.351 at iteration 7
#> Achieved score: 9.312 at iteration 15
#> Achieved score: 9.266 at iteration 37
#> Achieved score: 9.257 at iteration 38
#> Achieved score: 9.257 at iteration 42
#> Achieved score: 9.24 at iteration 47
#> Achieved score: 9.235 at iteration 83
plot_plate(bc$get_samples(), .col = Sex)