Introduction to Clustering of Local Indicators of Spatial Assocation (LISA) curves
9 February 2023
Package
spicyR 1.10.6
Contents
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'))
3.5 Using other clustering methods.
While the lisaClust function is convenient, we have not implemented an exhaustive
suite of clustering methods as it is very easy to do this yourself. There are
just two simple steps.
3.5.1 Generate LISA curves
We can calculate local indicators of spatial association (LISA) functions
using the lisa function. Here the LISA curves are a
localised summary of an L-function from a Poisson point process model. The radii
that will be calculated over can be set with Rs.
lisaCurves <- lisa(cellExp, Rs = c(20, 50, 100))3.5.2 Perform some clustering
The LISA curves can then be used to cluster the cells. Here we use k-means
clustering, other clustering methods like SOM could be used. We can store these
cell clusters or cell “regions” in our SegmentedCells object using the
cellAnnotation() <- function.
kM <- kmeans(lisaCurves,2)
cellAnnotation(cellExp, "region") <- paste('region',kM$cluster,sep = '_')3.6 Alternative hatching plot
We could also create this plot using geom_hatching and scale_region_manual.
df <- as.data.frame(cellSummary(cellExp))
p <- ggplot(df,aes(x = x,y = y, colour = cellType, region = region)) +
geom_point() +
facet_wrap(~imageID) +
geom_hatching(window = "concave",
line.spacing = 11,
nbp = 50,
line.width = 2,
hatching.colour = "gray20",
window.length = NULL) +
theme_minimal() +
scale_region_manual(values = 6:7, labels = c('ab','cd'))
p
## Faster ploting
The hatchingPlot can be quite slow for large images and high nbp or linewidth.
It is often useful to simply plot the regions without the cell type information.
df <- as.data.frame(cellSummary(cellExp))
df <- df[df$imageID == "s1", ]
p <- ggplot(df,aes(x = x,y = y, colour = region)) +
geom_point() +
theme_classic()
p
4 Using a SingleCellExperiment
The lisaClust function also works with a SingleCellExperiment. First lets
create a SingleCellExperiment object.
sce <- SingleCellExperiment(colData = cellSummary(cellExp))lisaClust just needs columns in colData corresponding to the x and y coordinates of the
cells, a column annotating the cell types of the cells and a column indicating
which image each cell came from.
sce <- lisaClust(sce,
k = 2,
spatialCoords = c("x", "y"),
cellType = "cellType",
imageID = "imageID")We can then plot the regions using the following.
hatchingPlot(sce)
5 Damond et al. islet data.
Here we apply our lisaClust framework to three images of pancreatic islets
from A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry by
Damond et al. (2019).
5.1 Read in data
We will start by reading in the data and storing it as a SegmentedCells
object. Here the data is in a format consistent with that outputted by
CellProfiler.
isletFile <- system.file("extdata","isletCells.txt.gz", package = "spicyR")
cells <- read.table(isletFile, header = TRUE)
cellExp <- SegmentedCells(cells, cellProfiler = TRUE)5.2 Cluster cell-types
This data does not include annotation of the cell-types of each cell. Here we
extract the marker intensities from the SegmentedCells object using
cellMarks. We then perform k-means clustering with eight clusters and store
these cell-type clusters in our SegmentedCells object using cellType() <-.
markers <- cellMarks(cellExp)
kM <- kmeans(markers,10)
cellType(cellExp) <- paste('cluster', kM$cluster, sep = '')5.3 Generate LISA curves
As before, we can calculate perform k-means clustering on the local indicators
of spatial association (LISA) functions using the lisaClust function.
cellExp <- lisaClust(cellExp, k = 2, Rs = c(10,20,50))These regions are stored in cellExp and can be extracted.
cellAnnotation(cellExp, "region") |>
head()
## [1] "region_1" "region_1" "region_1" "region_1" "region_1" "region_1"5.4 Examine cell type enrichment
We should check to see which cell types appear more frequently in each region than expected by chance.
regionMap(cellExp, type = "bubble")
5.5 Plot identified regions
Finally, we can use hatchingPlot to construct a ggplot object where the
regions are marked by different hatching patterns. This allows us to visualize
the two regions and ten cell-types simultaneously.
hatchingPlot(cellExp)
6 sessionInfo()
sessionInfo()
## R version 4.2.2 (2022-10-31)
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## Running under: Ubuntu 20.04.5 LTS
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