countInteractions: Summarizes cell-cell interactions within grouping levels...

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

Summarizes cell-cell interactions within grouping levels (e.g. images)

Description

Function to calculate the average number of neighbors B that a cell of type A has using different approaches.

Usage

countInteractions(
  object,
  group_by,
  label,
  colPairName,
  method = c("classic", "histocat", "patch"),
  patch_size = NULL
)

Arguments

object	a SingleCellExperiment or SpatialExperiment object .
group_by	a single character indicating the colData(object) entry by which interactions are grouped. This is usually the image ID or patient ID.
label	single character specifying the colData(object) entry which stores the cell labels. These can be cell-types labels or other metadata.
colPairName	single character indicating the colPair(object) entry containing cell-cell interactions in form of an edge list.
method	which cell-cell interaction counting method to use (see details)
patch_size	if method = "patch", a single numeric specifying the minimum number of neighbors of the same type to be considered a patch (see details)

Value

a DataFrame containing one row per group_by entry and unique label entry combination (from_label, to_label). The ct entry stores the interaction count as described in the details. NA is returned if a certain label is not present in this grouping level.

Counting and summarizing cell-cell interactions

In principle, the countInteractions function counts the number of edges (interactions) between each set of unique entries in colData(object)[[label]]. Simplified, it counts for each cell of type A the number of neighbors of type B. This count is averaged within each unique entry colData(object)[[group_by]] in three different ways:

1. method = "classic": The count is divided by the total number of cells of type A. The final count can be interpreted as "How many neighbors of type B does a cell of type A have on average?"

2. method = "histocat": The count is divided by the number of cells of type A that have at least one neighbor of type B. The final count can be interpreted as "How many many neighbors of type B has a cell of type A on average, given it has at least one neighbor of type B?"

3. method = "patch": For each cell, the count is binarized to 0 (less than patch_size neighbors of type B) or 1 (more or equal to patch_size neighbors of type B). The binarized counts are averaged across all cells of type A. The final count can be interpreted as "What fraction of cells of type A have at least a given number of neighbors of type B?"

Author(s)

Vito Zanotelli

Jana Fischer

adapted by Nils Eling ([e-mail])

References

Schulz, D. et al., Simultaneous Multiplexed Imaging of mRNA and Proteins with Subcellular Resolution in Breast Cancer Tissue Samples by Mass Cytometry., Cell Systems 2018 6(1):25-36.e5

Shapiro, D. et al., histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data, Nature Methods 2017 14, p. 873–876

See Also

testInteractions for testing cell-cell interactions per grouping level.

Examples

library(cytomapper)
data(pancreasSCE)

pancreasSCE <- buildSpatialGraph(pancreasSCE, img_id = "ImageNb", 
                                  type = "knn", k = 3)
                               
# Classic style calculation
(out <- countInteractions(pancreasSCE, 
                                group_by = "ImageNb",
                                label = "CellType", 
                                method = "classic",
                                colPairName = "knn_interaction_graph"))
                                
# Histocat style calculation
(out <- countInteractions(pancreasSCE, 
                                group_by = "ImageNb",
                                label = "CellType", 
                                method = "histocat",
                                colPairName = "knn_interaction_graph"))
                                
# Patch style calculation
(out <- countInteractions(pancreasSCE, 
                                group_by = "ImageNb",
                                label = "CellType", 
                                method = "patch",
                                patch_size = 3,
                                colPairName = "knn_interaction_graph"))
                                

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