1 Installation

if (!require("BiocManager"))
    install.packages("BiocManager")
BiocManager::install("lisaClust")

# load required packages
library(lisaClust)
library(spicyR)
library(ggplot2)
library(SingleCellExperiment)

2 Overview

Clustering local indicators of spatial association (LISA) functions is a methodology for identifying consistent spatial organisation of multiple cell-types in an unsupervised way. This can be used to enable the characterization of interactions between multiple cell-types simultaneously and can complement traditional pairwise analysis. In our implementation our LISA curves are a localised summary of an L-function from a Poisson point process model. Our framework lisaClust can be used to provide a high-level summary of cell-type colocalization in high-parameter spatial cytometry data, facilitating the identification of distinct tissue compartments or identification of complex cellular microenvironments.

3 Quick start

3.1 Generate toy data

TO illustrate our lisaClust framework, here we consider a very simple toy example where two cell-types are completely separated spatially. We simulate data for two different images.

set.seed(51773)
x <- round(c(runif(200),runif(200)+1,runif(200)+2,runif(200)+3,
           runif(200)+3,runif(200)+2,runif(200)+1,runif(200)),4)*100
y <- round(c(runif(200),runif(200)+1,runif(200)+2,runif(200)+3,
             runif(200),runif(200)+1,runif(200)+2,runif(200)+3),4)*100
cellType <- factor(paste('c',rep(rep(c(1:2),rep(200,2)),4),sep = ''))
imageID <- rep(c('s1', 's2'),c(800,800))

cells <- data.frame(x, y, cellType, imageID)

ggplot(cells, aes(x,y, colour = cellType)) + geom_point() + facet_wrap(~imageID)

3.2 Create SegmentedCellExperiment object

First we store our data in a SegmentedCells object.


cellExp <- SegmentedCells(cells, cellTypeString = 'cellType')

3.3 Running lisaCLust

We can then use a convience function lisaClust to simultaneously calculate local indicators of spatial association (LISA) functions using the lisa function and perform k-means clustering.

cellExp <- lisaClust(cellExp, k = 2)

3.4 Plot identified regions

The hatchingPlot function can be used to construct a ggplot object where the regions are marked by different hatching patterns. This allows us to plot both regions and cell-types on the same visualization.

hatchingPlot(cellExp, useImages = c('s1','s2'))

Introduction to Clustering of Local Indicators of Spatial Assocation (LISA) curves

10 August 2023

Package

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