minDistToCells: Function to calculate minimal distance to cells of interest

In BodenmillerGroup/imcRtools: Methods for imaging mass cytometry data analysis

Function to calculate minimal distance to cells of interest

Description

Function to return the distance of the closest cell of interest for each cell in the data. In the case of patched/clustered cells negative distances are returned by default which indicate the distance of the cells of interest to the closest cell that is not of the type of cells of interest.

Usage

minDistToCells(
  object,
  x_cells,
  img_id,
  name = "distToCells",
  coords = c("Pos_X", "Pos_Y"),
  return_neg = TRUE,
  BPPARAM = SerialParam()
)

Arguments

object	a SingleCellExperiment or SpatialExperiment object
x_cells	logical vector of length equal to the number of cells contained in object. TRUE entries define the cells to which distances will be calculated.
img_id	single character indicating the colData(object) entry containing the unique image identifiers.
name	character specifying the name of the colData entry to safe the distances in.
coords	character vector of length 2 specifying the names of the colData (for a SingleCellExperiment object) or the spatialCoords entries of the cells' x and y locations.
return_neg	logical indicating whether negative distances are to be returned for the distances of patched/spatially clustered cells.
BPPARAM	a BiocParallelParam-class object defining how to parallelize computations.

Value

returns an object of class(object) containing a new column entry to colData(object)[[name]]. Cells in the object are grouped by entries in img_id.

Ordering of the output object

The minDistToCells function operates on individual images. Therefore the returned object is grouped by entries in img_id. This means all cells of a given image are grouped together in the object. The ordering of cells within each individual image is the same as the ordering of these cells in the input object.

Author(s)

Daniel Schulz ([e-mail])

Examples

library(cytomapper)
data(pancreasSCE)

# Build interaction graph
pancreasSCE <- buildSpatialGraph(pancreasSCE, img_id = "ImageNb",
type = "expansion",threshold = 20)

# Detect patches of "celltype_B" cells
pancreasSCE <- patchDetection(pancreasSCE,
                             img_id = "ImageNb",
                             patch_cells = pancreasSCE$CellType == "celltype_B",
                             colPairName = "expansion_interaction_graph",
                             min_patch_size = 20,
                             expand_by = 1)

plotSpatial(pancreasSCE, 
            img_id = "ImageNb", 
            node_color_by = "patch_id",
            scales = "free")

# Distance to celltype_B patches
pancreasSCE <- minDistToCells(pancreasSCE,
                             x_cells = !is.na(pancreasSCE$patch_id),
                             coords = c("Pos_X","Pos_Y"),
                             img_id = "ImageNb")

plotSpatial(pancreasSCE,
            img_id = "ImageNb",
            node_color_by = "distToCells",
            scales = "free")

BodenmillerGroup/imcRtools documentation built on Oct. 30, 2024, 12:19 p.m.