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

find_nearest_distance(csd, phenotypes = NULL)

Arguments

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(csd$Phenotype) will be used.

Value

A data_frame containing a Distance to <phenotype> column for each phenotype. Will contain NA values where there is no other cell of the phenotype.

See also

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, distance_matrix, spatial_distribution_report, subset_distance_matrix

Examples

# Compute distance columns csd <- sample_cell_seg_data nearest <- find_nearest_distance(csd) dplyr::glimpse(nearest)
#> Observations: 6,072 #> Variables: 5 #> $ `Distance to CD68+` <dbl> 29.529646, 30.269622, 38.082148, 36.674242, 15... #> $ `Distance to CD8+` <dbl> 18.03469, 50.09241, 64.37585, 67.57403, 60.002... #> $ `Distance to CK+` <dbl> 36.830694, 21.377558, 109.317199, 3.605551, 10... #> $ `Distance to FoxP3+` <dbl> 16.347783, 24.909837, 40.140379, 30.870698, 26... #> $ `Distance to other` <dbl> 10.307764, 6.800735, 8.062258, 19.811613, 5.59...
# 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 `data_frame` 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(.))) # }