Count cells within a radius for multiple tissue categories, phenotypes and fields.

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
)

Arguments

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.

Value

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.

Details

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.

See also

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()

Examples

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)
#> Processing Set4_1-6plex_[16142,55840] 
#> # A tibble: 8 x 10
#>   slide_id     source  category from  to    radius from_count to_count from_with
#>   <chr>        <chr>   <chr>    <chr> <chr>  <dbl>      <int>    <int>     <int>
#> 1 Set4_1-6plex Set4_1~ Tumor    CK+   CD68+     10       2192      101        79
#> 2 Set4_1-6plex Set4_1~ Tumor    CK+   CD68+     25       2192      101       397
#> 3 Set4_1-6plex Set4_1~ Tumor    CK+   CD8+      10       2192       51        63
#> 4 Set4_1-6plex Set4_1~ Tumor    CK+   CD8+      25       2192       51       320
#> 5 Set4_1-6plex Set4_1~ Stroma   CK+   CD68+     10         65      316         6
#> 6 Set4_1-6plex Set4_1~ Stroma   CK+   CD68+     25         65      316        34
#> 7 Set4_1-6plex Set4_1~ Stroma   CK+   CD8+      10         65      177         4
#> 8 Set4_1-6plex Set4_1~ Stroma   CK+   CD8+      25         65      177        22
#> # ... with 1 more variable: within_mean <dbl>

# 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)
#> Processing Set4_1-6plex_[16142,55840] 
#> # A tibble: 2 x 10
#>   slide_id     source  category from  to    radius from_count to_count from_with
#>   <chr>        <chr>   <chr>    <chr> <chr>  <dbl>      <int>    <int>     <int>
#> 1 Set4_1-6plex Set4_1~ all      CK+   T ce~     10       2257      456       150
#> 2 Set4_1-6plex Set4_1~ all      CK+   T ce~     25       2257      456       824
#> # ... with 1 more variable: within_mean <dbl>