mk_plate_scoring_functions {designit}

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

Description

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

Usage

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$new(
  dimensions = c("column" = 6, "row" = 10)
)
bc <- assign_random(bc, invivo_study_samples)
scoring_f <- mk_plate_scoring_functions(
  bc,
  row = "row", column = "column", group = "Sex"
)
bc <- optimize_design(bc, scoring = scoring_f, max_iter = 100)
plot_plate(bc$get_samples(), .col = Sex)