count_within_batch: Count cells within a radius for multiple tissue categories,...
In akoyabio/phenoptr: inForm Helper Functions

Description Usage Arguments Details Value See Also Examples

This is a batch version of count_within(). Given the path to a directory containing cell seg data files, for each given tissue category, pair of 'from' phenotype and 'to' phenotype, and radius, it counts the number of 'from' cells having a 'to' cell within radius microns.

count_within_batch(
  base_path,
  pairs,
  radius,
  category = NA,
  phenotype_rules = NULL,
  verbose = TRUE
)

`base_path`	Path to a directory containing at least one `_cell_seg_data.txt` file.
`pairs`	A list of pairs of phenotypes. Each entry is a two-element vector. The result will contain values for each pair.
`radius`	The radius or radii to search within.
`category`	Optional tissue categories to restrict both `from` and `to` phenotypes.
`phenotype_rules`	(Optional) A named list. Item names are phenotype names and must match entries in `pairs`. Item values are selectors for `select_rows()`.
`verbose`	If TRUE, display progress.

The category parameter may be a single category or a list of categories.

See the tutorial Selecting cells within a cell segmentation table for more on the use of pairs and phenotype_rules.

A tibble containing these columns:

slide_id: Slide ID from the data files, if available.
source: Base file name of the source file with _cell_seg_data.txt stripped off for brevity.
category: Tissue category, if provided as a parameter, or "all".
from: From phenotype.
to: To phenotype.
radius, from_count, to_count, from_with, within_mean: Results from count_within for this data file and tissue category.

Other distance functions: compute_all_nearest_distance(), count_touching_cells(), count_within_many(), count_within(), distance_matrix(), find_nearest_distance(), spatial_distribution_report(), subset_distance_matrix()

base_path <- sample_cell_seg_folder()

# Count tumor cells near macrophages, and tumor cells near CD8 separately,
# in tumor and stroma tissue categories separately.
pairs <- list(c('CK+', 'CD68+'),
             c('CK+', 'CD8+'))
radius <- c(10, 25)
category <- list('Tumor', 'Stroma')
count_within_batch(base_path, pairs, radius, category)

# Count tumor cells near any T cell in all tissue categories.
# Use `phenotype_rules` to define the T cell phenotype
pairs <- c('CK+', 'T cell')
rules <- list(
'T cell'=c('CD8+', 'FoxP3+'))
count_within_batch(base_path, pairs, radius, phenotype_rules=rules)