find_nearest_distance: Nearest neighbor distances for each cell and phenotype.
In akoyabio/phenoptr: inForm Helper Functions

Description Usage Arguments Details Value See Also Examples

For each cell in a single sample, find the distances from the cell to the nearest neighbor cells in each of the provided phenotypes.

1	find_nearest_distance(csd, phenotypes = NULL, dst = NULL)

`csd`	A data frame with `Cell X Position`, `Cell Y Position` and `Phenotype` columns, such as the result of calling read_cell_seg_data.
`phenotypes`	Optional list of phenotypes to include. If omitted, `unique_phenotypes(csd)` will be used.
`dst`	Optional distance matrix. If provided, this should be `distance_matrix(csd)`. Not used if `rtree` is available.

If the rtree package is available, this will use a fast, memory-efficient algorithm capable of processing fields with many thousand cells. Otherwise, a simple distance matrix algorithm is used. The simple algorithm requires at least 8 * (number of cells)^2 bytes of memory which becomes prohibitive as the number of cells becomes large.

Install the rtree package from GitHub using the command devtools::install_github('akoyabio/rtree').

A tibble containing a Distance to <phenotype> column and Cell ID <phenotype> column for each phenotype. Columns will contain NA values where there is no other cell of the phenotype.

compute_all_nearest_distance which applies this function to a (possibly merged) data file.

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

# Compute distance columns
csd <- sample_cell_seg_data
nearest <- find_nearest_distance(csd)
dplyr::glimpse(nearest)

# Make a combined data frame including original data and distance columns
csd <- cbind(csd, find_nearest_distance(csd))

## Not run: 
# If `merged` is a data frame containing cell seg data from multiple fields,
# this code will create a new `tibble` with distance columns computed
# for each `Sample Name` in the data.
merged_with_distance <- merged %>%
  dplyr::group_by(`Sample Name`) %>%
  dplyr::do(dplyr::bind_cols(., find_nearest_distance(.)))

## End(Not run)