Introduction to Clustering of Local Indicators of Spatial Assocation (LISA) curves
26 October 2023
Package
spicyR 1.15.1
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) + theme_minimal()
3.2 Create Single Cell Experiment object
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):3.3 Running lisaCLust
We can then use the convenience function lisaClust to simultaneously
calculate local indicators of spatial association (LISA) functions using
the lisa function 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.
These clusters are stored in colData of the SingleCellExperiment
object, as a new column with the column name 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_23.4 Plot identified regions
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'))
## 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.
3.4.1 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(SCE, Rs = c(20, 50, 100))3.4.2 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 SingleCellExperiment object using the
cellAnnotation() <- function.
# Custom clustering algorithm
kM <- kmeans(lisaCurves,2)
# Storing clusters into colData
colData(SCE)$custom_region <- paste('region',kM$cluster,sep = '_')
colData(SCE) |> head()
## DataFrame with 6 rows and 6 columns
## x y cellType imageID region custom_region
## <numeric> <numeric> <factor> <character> <character> <character>
## 1 36.72 38.58 c1 s1 region_2 region_2
## 2 61.38 41.29 c1 s1 region_2 region_2
## 3 33.59 80.98 c1 s1 region_2 region_2
## 4 50.17 64.91 c1 s1 region_2 region_2
## 5 82.93 35.60 c1 s1 region_2 region_2
## 6 83.13 2.69 c1 s1 region_2 region_24 Damond et al. islet data.
Next, 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
SingleCellExperiment 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)
damonSCE <- SingleCellExperiment(assay = list(intensities = t(cells[,grepl(names(cells), pattern = "Intensity_")])),
colData = cells[,!grepl(names(cells), pattern = "Intensity_")]
)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 SingleCellExperiment object
using assay(). We then perform k-means clustering with 10
clusters and store these cell-type clusters in our SingleCellExperiment
object using colData() <-.
markers <- t(assay(damonSCE, "intensities"))
kM <- kmeans(markers,10)
colData(damonSCE)$cluster <- paste('cluster', kM$cluster, sep = '')
colData(damonSCE)[, c("ImageNumber", "cluster")] |> head()
## DataFrame with 6 rows and 2 columns
## ImageNumber cluster
## <integer> <character>
## 1 1 cluster9
## 2 1 cluster9
## 3 1 cluster9
## 4 1 cluster5
## 5 1 cluster5
## 6 1 cluster24.3 Generate LISA curves
As before, we can perform k-means clustering on the local
indicators of spatial association (LISA) functions using the lisaClust
function, remembering to specify the imageID, cellType, and spatialCoords
columns in colData.
damonSCE <- lisaClust(damonSCE,
k = 2,
Rs = c(10,20,50),
imageID = "ImageNumber",
cellType = "cluster",
spatialCoords = c("Location_Center_X", "Location_Center_Y"))These regions are stored in colData and can be extracted.
colData(damonSCE)[, c("ImageNumber", "region")] |>
head(20)
## DataFrame with 20 rows and 2 columns
## ImageNumber region
## <integer> <character>
## 1 1 region_1
## 2 1 region_1
## 3 1 region_1
## 4 1 region_1
## 5 1 region_1
## ... ... ...
## 16 1 region_1
## 17 1 region_1
## 18 1 region_1
## 19 1 region_1
## 20 1 region_14.4 Examine cell type enrichment
lisaClust also provides a convenient function, regionMap, for examining which
cell types are located in which regions. In this example, we use this to check
which cell types appear more frequently in each region than expected by chance.
Here, we clearly see that clusters 2, 5, 1, and 8 are highly concentrated in region 1, whilst all other clusters are thinly spread out across region 2.
We can further segregate these cells by increasing the number of clusters, ie.
increasing the parameter k = in the lisaClust() function, but for the purposes
of demonstration, let’s take a look at the hatchingPlot of these regions.
regionMap(damonSCE,
imageID = "ImageNumber",
cellType = "cluster",
spatialCoords = c("Location_Center_X", "Location_Center_Y"),
type = "bubble")
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(damonSCE,
cellType = "cluster",
spatialCoords = c("Location_Center_X", "Location_Center_Y"))
5 sessionInfo()
sessionInfo()
## R Under development (unstable) (2023-10-22 r85388)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SingleCellExperiment_1.25.0 SummarizedExperiment_1.33.0
## [3] Biobase_2.63.0 GenomicRanges_1.55.0
## [5] GenomeInfoDb_1.39.0 IRanges_2.37.0
## [7] S4Vectors_0.41.1 BiocGenerics_0.49.0
## [9] MatrixGenerics_1.15.0 matrixStats_1.0.0
## [11] ggplot2_3.4.4 spicyR_1.15.1
## [13] lisaClust_1.11.1 BiocStyle_2.31.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 deldir_1.0-9
## [3] rlang_1.1.1 magrittr_2.0.3
## [5] compiler_4.4.0 spatstat.geom_3.2-7
## [7] mgcv_1.9-0 fftwtools_0.9-11
## [9] vctrs_0.6.4 reshape2_1.4.4
## [11] stringr_1.5.0 pkgconfig_2.0.3
## [13] SpatialExperiment_1.13.0 crayon_1.5.2
## [15] fastmap_1.1.1 backports_1.4.1
## [17] magick_2.8.1 XVector_0.43.0
## [19] labeling_0.4.3 utf8_1.2.4
## [21] rmarkdown_2.25 nloptr_2.0.3
## [23] purrr_1.0.2 xfun_0.40
## [25] MultiAssayExperiment_1.29.0 zlibbioc_1.49.0
## [27] cachem_1.0.8 jsonlite_1.8.7
## [29] goftest_1.2-3 DelayedArray_0.29.0
## [31] tweenr_2.0.2 spatstat.utils_3.0-4
## [33] BiocParallel_1.37.0 broom_1.0.5
## [35] parallel_4.4.0 R6_2.5.1
## [37] bslib_0.5.1 stringi_1.7.12
## [39] RColorBrewer_1.1-3 spatstat.data_3.0-3
## [41] boot_1.3-28.1 car_3.1-2
## [43] numDeriv_2016.8-1.1 jquerylib_0.1.4
## [45] Rcpp_1.0.11 bookdown_0.36
## [47] knitr_1.44 tensor_1.5
## [49] splines_4.4.0 Matrix_1.6-1.1
## [51] tidyselect_1.2.0 abind_1.4-5
## [53] yaml_2.3.7 codetools_0.2-19
## [55] spatstat.random_3.2-1 spatstat.explore_3.2-5
## [57] curl_5.1.0 lmerTest_3.1-3
## [59] lattice_0.22-5 tibble_3.2.1
## [61] plyr_1.8.9 withr_2.5.1
## [63] evaluate_0.22 survival_3.5-7
## [65] polyclip_1.10-6 pillar_1.9.0
## [67] BiocManager_1.30.22 ggpubr_0.6.0
## [69] carData_3.0-5 ClassifyR_3.7.0
## [71] generics_0.1.3 RCurl_1.98-1.12
## [73] munsell_0.5.0 scales_1.2.1
## [75] minqa_1.2.6 class_7.3-22
## [77] glue_1.6.2 pheatmap_1.0.12
## [79] tools_4.4.0 data.table_1.14.8
## [81] lme4_1.1-34 ggsignif_0.6.4
## [83] grid_4.4.0 tidyr_1.3.0
## [85] colorspace_2.1-0 nlme_3.1-163
## [87] GenomeInfoDbData_1.2.11 ggforce_0.4.1
## [89] cli_3.6.1 spatstat.sparse_3.0-3
## [91] fansi_1.0.5 S4Arrays_1.3.0
## [93] scam_1.2-14 dplyr_1.1.3
## [95] concaveman_1.1.0 V8_4.4.0
## [97] gtable_0.3.4 rstatix_0.7.2
## [99] sass_0.4.7 digest_0.6.33
## [101] SparseArray_1.3.0 farver_2.1.1
## [103] rjson_0.2.21 htmltools_0.5.6.1
## [105] lifecycle_1.0.3 MASS_7.3-60.1