Identify and visualise regions of cell type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution.
lisaClust 1.15.4
if (!require("BiocManager")) {
install.packages("BiocManager")
}
BiocManager::install("lisaClust")
# load required packages
library(lisaClust)
library(spicyR)
library(ggplot2)
library(SingleCellExperiment)
library(SpatialDatasets)
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.
To illustrate our lisaClust
framework, 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) +
theme_minimal()
First we store our data in a SingleCellExperiment
object.
SCE <- SingleCellExperiment(colData = cells)
SCE
## class: SingleCellExperiment
## dim: 0 1600
## metadata(0):
## assays(0):
## rownames: NULL
## rowData names(0):
## colnames: NULL
## colData names(4): x y cellType imageID
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
We can then use the convenience function lisaClust
to simultaneously
calculate local indicators of spatial association (LISA) functions and perform
k-means clustering. The number of clusters can be specified with the k =
parameter.
In the example below, we’ve chosen k = 2
, resulting in a total of 2 clusters. The cell type column can be specified using the cellType =
argument. By default, lisaClust
uses the column named cellType
.
The clusters identified by lisaClust
are stored in colData
of the SingleCellExperiment
object as a new column called regions
.
SCE <- lisaClust(SCE, k = 2)
colData(SCE) |> head()
## DataFrame with 6 rows and 5 columns
## x y cellType imageID region
## <numeric> <numeric> <factor> <character> <character>
## 1 36.72 38.58 c1 s1 region_2
## 2 61.38 41.29 c1 s1 region_2
## 3 33.59 80.98 c1 s1 region_2
## 4 50.17 64.91 c1 s1 region_2
## 5 82.93 35.60 c1 s1 region_2
## 6 83.13 2.69 c1 s1 region_2
lisaClust
also provides the convenient hatchingPlot
function to
visualise the different regions that have been demarcated by the
clustering. hatchingPlot
outputs a ggplot
object where the regions
are marked by different hatching patterns. In a real biological dataset,
this allows us to plot both regions and cell-types on the same
visualization.
In the example below, we can visualise our stimulated data where our 2
cell types have been separated neatly into 2 distinct regions based on
which cell type each region is dominated by. region_2
is dominated by
the red cell type c1
, and region_1
is dominated by the blue cell
type c2
.
hatchingPlot(SCE, useImages = c("s1", "s2"))