Clustering of Local Indicators of Spatial Assocation (LISA) curves
19 May 2021
Package
spicyR 1.4.0
Contents
1 Installation
if (!require("BiocManager"))
install.packages("BiocManager")
BiocManager::install("spicyR")# load required packages
library(spicyR)
library(lisaClust)
library(ggplot2)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 Generate LISA curves
We can then 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))3.4 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
region() <- function.
kM <- kmeans(lisaCurves,2)
region(cellExp) <- paste('region',kM$cluster,sep = '_')3.5 Plot identified regions
The hatchingPlot function can be used to construct a ggplot object where the
regions are marked by different hatching patterns. This allows us to plot both
regions and cell-types on the same visualization.
hatchingPlot(cellExp, imageID = c('s1','s2'))
3.6 Alternative hatching plot
We could also create this plot using geom_hatching and scale_region_manual.
df <- region(cellExp, annot = TRUE)
p <- ggplot(df,aes(x = x,y = y, colour = cellType, region = region)) +
geom_point() +
facet_wrap(~imageID) +
geom_hatching(window = "concave",
line.spacing = 11,
nbp = 50,
line.width = 2,
hatching.colour = "gray20",
window.length = 0.1) +
theme_minimal() +
scale_region_manual(values = 6:7, labels = c('ab','cd'))
p
4 Damond et al. islet data.
Here we apply our lisaClust framework to three images of pancreatic islets
from A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry by
Damond et al. (2019).
4.1 Read in data
We will start by reading in the data and storing it as a SegmentedCells
object. Here the data is in a format consistent with that outputted by
CellProfiler.
isletFile <- system.file("extdata","isletCells.txt.gz", package = "spicyR")
cells <- read.table(isletFile, header = TRUE)
cellExp <- SegmentedCells(cells, cellProfiler = TRUE)4.2 Cluster cell-types
This data does not include annotation of the cell-types of each cell. Here we
extract the marker intensities from the SegmentedCells object using
cellMarks. We then perform k-means clustering with eight clusters and store
these cell-type clusters in our SegmentedCells object using cellType() <-.
markers <- cellMarks(cellExp)
kM <- kmeans(markers,10)
cellType(cellExp) <- paste('cluster', kM$cluster, sep = '')4.3 Generate LISA curves
As before, we can calculate local indicators of spatial association (LISA)
functions using the lisa function.
lisaCurves <- lisa(cellExp, Rs = c(10,20,50))4.4 Perform some clustering
The LISA curves can then be used to cluster the cells. Here we use k-means
clustering to cluster the cells into two microenvironments.
We can store these cell clusters or cell “regions” in our SegmentedCells
object using the region() <- function.
kM <- kmeans(lisaCurves,2)
region(cellExp) <- paste('region',kM$cluster,sep = '_')4.5 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)
5 sessionInfo()
sessionInfo()
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