Spatial statistics builds around the first law of geography of Tobler (1970) that states “everything is related to everything else, but near things are more related than distant things”. This dependence of spatial observations has been studied in the field of (geo)spatial statistics. In general, spatial dependence is estimated by comparing the values at one location with the values at another location that is a given distance (‘spatial lag’) away (Dale and Fortin 2014; Baddeley, Rubak, and Turner 2015).
The two technological streams, imaging- and sequencing-based assays, are very different in terms of data modalities:
In the following vignette, the two main exploratory spatial statistics streams, point pattern analysis and lattice data analysis will be introduced.
library(dplyr)
library(scran)
library(spdep)
library(tidyr)
library(ggplot2)
library(Voyager)
library(SFEData)
library(spatstat)
library(openxlsx)
library(spatialFDA)
library(BiocParallel)
library(STexampleData)
library(SpatialExperiment)
library(SpatialFeatureExperiment)
# specify whether/how to
# perform parallelization
bp <- MulticoreParam(4)
# set seed for random number generation
# in order to make results reproducible
set.seed(77)# load dataset as SPE & convert to SFE
spe <- Janesick_breastCancer_Xenium_rep1()
sfe <- toSpatialFeatureExperiment(spe)
# load the official 10X annotations
fnm <- "https://cdn.10xgenomics.com/raw/upload/v1695234604/Xenium%20Preview%20Data/Cell_Barcode_Type_Matrices.xlsx"
labels <- read.xlsx(fnm, sheet=4)
labels$cell_id <- (labels$Barcode)
# add the cell type labels to the spe
cd <- as.data.frame(colData(sfe))
cd <- left_join(cd,
as.data.frame(labels),
by=join_by("cell_id"))
colData(sfe) <- DataFrame(cd)
# exclude 0-count cells &
# cells without annotation
sfe <- sfe[, colSums(counts(sfe)) > 0]
sfe <- sfe[, !is.na(sfe$Cluster)]
# log-library size normalization
sfe <- logNormCounts(sfe)
# basic theme for spatial plots
xy <- spatialCoords(sfe)
theme_xy <- list(
coord_equal(expand=FALSE),
theme_void(), theme(
plot.margin=margin(l=5),
legend.key=element_blank(),
panel.background=element_rect(fill="black")))In this chapter, we will use the human breast cancer Xenium dataset (Janesick et al. 2023), which we loaded above (using the STexampleData package (Righelli et al. 2022)) and converted into a SpatialFeatureExperiment object. The SpatialFeatureExperiment package integrates the SpatialExperiment class with geometric annotations that are represented as Simple Features in the sf packages. We will use the Voyager package (Moses et al. 2023), which provides convenient wrappers around classic geospatial R packages such as spdep (Pebesma and Bivand 2023). For more details, please consult the authors’ comprehensive vignettes.
This part is based on the pasta resource vignette on lattice data analysis (Emons et al. 2025).
Lattice based spatial methods rely on the concept of neighborhood. The neighborhood defines the spatial dependency between locations. Therefore, the first step of lattice based spatial analysis is the construction of a spatial weight matrix. Here we will use a nearest neighbor-based approach.
Different methods for the construction of the weight matrix exist, such as
A detailed overview can be found in the documentation of the spdep package.
As different weight matrices influence downstream results, analysts should justify their choice of the weight matrix. A more detailed overview can be found in Pebesma and Bivand (2023).
# construct (K=6)NN-graph using cell centroids
knn6 <- findSpatialNeighbors(sfe, type="centroids", method="knearneigh", k=6)
colGraph(sfe, "knn6") <- knn6
# visualize across tissue section
plotColGraph(sfe,
colGraphName="knn6",
colGeometryName="centroids",
segment_size=0.1,
geometry_size=0.1) +
theme_void()
Spatial autocorrelation measures similarity between spatial units (e.g., cells) while recognize that the units are not independent due to their spatial context. Spatial autocorrelation metrics can be global (summarizing the entire study area) of view or local (provide statistic for each unit). In addition, there exist methods for univariate and multivariate comparisons of continuous and categorical data (Pebesma and Bivand 2023).
Moran’s \(I\) can be interpreted as the Pearson correlation between the value at a certain location and the average values of its neighbors. The global value is a weighted average of the respective local values (Moran 1950).
## [1] 313sfe <- runUnivariate(sfe,
type="moran",
features=geneProbes,
colGraphName="knn6",
BPPARAM=bp)We can visualize the three genes with highest Moran’s \(I\).
I <- rowData(sfe)$moran_sample01
o <- order(I, decreasing=TRUE)
topGenes <- rownames(sfe)[head(o, 3)]
plotSpatialFeature(sfe, topGenes, ncol=3)
We can further visualize this using when calculating and plotting local Moran’s \(I\) values (Anselin 1995). The interpretation is analogue to the global counterpart. The higher the value, the more similar the expression among a cell’s neighbors. Negative values indicate local dissimilarity in expression.
sfe <- runUnivariate(sfe,
type="localmoran",
features=topGenes,
colGraphName="knn6",
BPPARAM=bp)
plotLocalResult(sfe,
name="localmoran",
features=topGenes,
colGeometryName="centroids",
divergent=TRUE,
diverge_center=0,
ncol=3)
During the interpretation of local autocorrelation measures, both the effect size (the value of the statistic) and the significance level should be considered. Because we calculate significance on each spot individually, values should be corrected for multiple testing.
plotLocalResult(sfe,
name="localmoran",
features=topGenes,
attribute="-log10p_adj",
colGeometryName="centroids",
divergent=TRUE,
diverge_center=0,
ncol=3)
Lee’s \(L\) combines the Pearson correlation coefficient and Moran’s \(I\) (Lee 2001). This unified metric allows to evaluate how two continuous variables are related while accounting for their spatial dependencies. We will calculate the global metric on the 20 most highly (non-spatial) variable features.
res <- calculateBivariate(sfe, type="lee", feature1=geneProbes, colGraphName="knn6")We will identify the gene pairs with highest Lee’s \(L\) value and then calculate and visualize the respective local measure.
# select the 20 highest values in the res matrix
val <- tail(sort(res), 20)[1]
genePairs <- which(res >= val, arr.ind=TRUE)
# keep only pairs containing different genes
genePairs <- genePairs[genePairs[,1] != genePairs[,2], ]
data.frame(
val=res[genePairs],
i=rownames(res)[genePairs[,1]],
j=colnames(res)[genePairs[,2]])## val i j
## 1 0.6436054 FOXA1 EPCAM
## 2 0.6669321 KRT7 EPCAM
## 3 0.6436054 EPCAM FOXA1
## 4 0.6308081 KRT7 FOXA1
## 5 0.6669321 EPCAM KRT7
## 6 0.6308081 FOXA1 KRT7
## 7 0.6416129 KRT8 KRT7
## 8 0.6416129 KRT7 KRT8sfe <- runBivariate(
sfe,
type="locallee",
colGraphName= "knn6",
feature1=c("EPCAM", "KRT7"))
plotLocalResult(
sfe,
name="locallee",
features="KRT7__EPCAM",
colGeometryName="centroids",
divergent=TRUE, diverge_center=0)
Join count statistics can be used to quantify the arrangement of categorical marks on lattices. The join count statistic calculates how often a categorical mark appears next to itself or to another category within the pre-defined neighborhood. This values can then be compared against a theoretical or a permutation-based value test of the null hypothesis of random spatial allocation of the marks (Getis 2009).
Here, we will use the this to quantify the tendency of clusters (non-spatial) to co-localize in their relative neighborhood. Note that the output contains z-scores and no p-values such that a user-defined threshold can be applied.
# code adapted from
# https://robinsonlabuzh.github.io/
# pasta/04-imaging-multivar-latSOD.html
# dependencies
library(BiocNeighbors)
library(BiocSingular)
library(bluster)
library(scater)
# log-library size normalization
sfe <- logNormCounts(sfe)
# set seed for random number generation
# in order to make results reproducible
set.seed(123)
# run PCA on the sample
sfe <- runPCA(sfe, exprs_values="logcounts", ncomponents=50)
# cluster based on first 10 PC's
# using Leiden community detection
pcs <- reducedDim(sfe, "PCA")[, 1:10]
params <- KNNGraphParam(
k=20,
cluster.fun="leiden",
cluster.args=list(
resolution=0.3,
objective_function="modularity"))
colData(sfe)$cluster <- clusterRows(pcs, BLUSPARAM=params)
# visualize cluster assignments
plotSpatialFeature(sfe,
features="cluster", colGeometryName="centroids") +
guides(col=guide_legend(override.aes=list(size=2)))
resJc <- joincount.multi(as.factor(sfe$cluster), colGraph(sfe, "knn6"))
resJc <- resJc[order(resJc[, "z-value"], decreasing=TRUE), ]
head(resJc, 20)## Joincount Expected Variance z-value
## 4:4 14910.0833 3836.16091 383.08992 565.784589
## 3:3 10405.4167 2077.05255 235.08187 543.187966
## 1:1 1985.0000 112.32999 16.22122 464.964203
## 5:5 2250.0833 260.53248 36.26822 330.363413
## 8:8 9711.9167 3652.23050 369.06495 315.426904
## 6:6 2567.8333 468.89491 62.89311 264.666011
## 7:7 4466.4167 1480.49383 176.49437 224.756962
## 9:9 391.8333 61.85256 9.09270 109.431461
## 5:3 2839.5833 1471.35090 186.35733 100.227440
## 2:2 1480.5833 661.75293 86.31994 88.132969
## 8:2 4698.1667 3109.40924 364.44710 83.222420
## 7:2 2962.8333 1979.72857 249.88363 62.191481
## 9:8 1546.4167 950.69679 116.58886 55.171345
## 9:7 1058.5000 605.29878 80.41584 50.538260
## 9:2 689.5833 404.68703 56.16565 38.014689
## 7:6 1888.9167 1666.47278 212.86306 15.246506
## 6:2 1236.5833 1114.16038 148.32507 10.052071
## 8:7 4509.8333 4650.80788 527.08776 -6.140436
## 9:6 283.0833 340.65272 47.92377 -8.316030
## 8:6 2441.5000 2617.40217 310.01770 -9.990287We note that the non-spatial cluster labels are of course most likely found next to each other. Apart from this obvious result, the first top non-self interaction is \(5:3\). Looking at the plot of the spatial distributions of the clusters these are two clusters in the ductal carcinoma regions of the tissue, the inner (blue) and the outer (green) lining.
Point pattern analysis is a subfield of spatial statistics that focuses on the representation of spatial locations of events or objects as points (Baddeley, Rubak, and Turner 2015) (p. 3). There are two main ways how to summarize cells as points (Emons et al. 2025):
The central R package to perform point pattern analysis is called spatstat (Baddeley and Turner 2005). The package spatialFDA creates an interface between SpatialExperiment objects and the spatstat library for easy integration into analysis workflows.
Part of this vignette is based on the pasta overview vignette and another vignette (Emons et al. 2025)
ppp object
The central object in spatstat is called ppp. This object contains three attributes:
In the following we will work with a Xenium dataset of breast cancer (Janesick et al. 2023). The representation of cells as points is done via cell centroids.
In the plot below, the centroids of the cells are attributed with a discrete cell type mark.
df <- data.frame(xy, colData(sfe))
ggplot(df, aes(x_centroid, y_centroid, col=Cluster)) +
guides(col=guide_legend(override.aes=list(size=2))) +
theme_xy + theme(legend.key.size=ggplot2::unit(0, "pt")) +
geom_point(shape=16, size=0.1) 
The first property to assess in a point pattern is the intensity. For a window \(W\) and points \(x\), the average intensity \(\bar{\lambda}\) is defined as the number of points \(n(x)\) divided by the area of the window \(|W|\):
\[ \bar{\lambda}=\frac{n(x)}{|W|} \]
The intensity of the points can be uniform in space which is called homogeneous. If the intensity is not uniform in space, it is called inhomogeneous. This distinction has important implications for the choice of the spatial metrics. Most metrics have a correction for inhomogeneous intensity of points (Baddeley, Rubak, and Turner 2015) (p. 157 onwards).
In the plot above we see that the intensity of all points is not uniform. This inhomogeneity of points has to be taken into accounts when interpreting spatial statistics metrics. For example, an indication of clustering in a point pattern can be due solely to an inhomogeneity of points. This is called the confounding between intensity and interaction (Baddeley, Rubak, and Turner 2015) (p. 151 onwards).
Global analyses summarize a statistic across the entire field of view. This means it reflects an average statistic and might not be reflective of local heterogeneities (Emons et al. 2025).
One option in point pattern analysis is to analyse the correlation of marks. Like this, e.g., a clustering or spacing of cells can be determined relative to a completely spatially random (CSR) process.
Complete spatial randomness (CSR) is the null scenario for a point pattern. It is characterized by two key properties:
(Baddeley, Rubak, and Turner 2015) (p. 199 onwards)
Ripley’s \(K\) is a well established function to assess correlation in a point pattern. It can be calculated within a mark or across marks. In essence, Ripley’s \(K\) quantifies the average number of points that fall in a \(r\)-neighborhood of a chosen mark (Ripley 1976; Baddeley, Rubak, and Turner 2015) (p. 132 onwards).
resCross <- calcCrossMetricPerFov(
sfe,
selection=c("DCIS_1", "DCIS_2", "Invasive_Tumor"),
subsetby="sample_id",
fun="Kcross",
marks="Cluster",
rSeq=seq(0, 500, l=100),
by="sample_id")We can plot Ripley’s \(K\) function not corrected for inhomogeneities of the chosen marks. Here, the diagonal is Ripley’s \(K\) function among the three cell types themselves and the off diagonal plots show the cross type combinations.
plotCrossMetricPerFov(
metricDf=resCross,
theo=TRUE,
correction="border",
x="r",
imageId="sample_id")## [[1]]
We note that all cell types, ductal carcinoma in situ 1 (DCIS 1), ductal carcinoma in situ 2 (DCIS 2), and invasive tumor cells show a clear interaction among themselves. DCIS 1 and invasive tumor cells show spacing, meaning there are fewer invasive tumor cells found in DCIS 1 than expect under complete spatial randomness (CSR). However, DCIS 2 and invasive tumor cells show a distribution that is completely spatially random in a \(500 µm\) \(r\)-neighborhood.
A complementary approach to correlation analysis is spacing. There are three main distance types (Baddeley, Rubak, and Turner 2015) (p. 255):
The nearest neighbor function \(G\) quantifies the average nearest-neighbor distance over a radius range \(r\) (Baddeley, Rubak, and Turner 2015) (p. 262).
resCross <- calcCrossMetricPerFov(
sfe,
selection=c("DCIS_1", "DCIS_2", "Invasive_Tumor"),
subsetby="sample_id",
fun="Gcross",
marks="Cluster",
rSeq=seq(0, 100, l=100),
by="sample_id")
plotCrossMetricPerFov(
resCross,
theo=TRUE,
correction="km",
x="r",
imageId="sample_id")[[1]]
In terms of spacing, the interpretation is a bit different. Still, the diagonal shows that all cell types are more clustered than expected if the patterns were completely spatially random. Comparing now DCIS 1 and DCIS 2 with invasive tumor cells, we see that both are more spaced than expect at random. However, the spacing is stronger for DCIS 1 and invasive tumor cells. At small radii, DCIS 2 and invasive tumor are distributed close to random.
The analysis with Ripley’s \(K\) and the \(G\) function are not contradictory. \(G\) functions summarize shorter scale interactions than \(K\) functions (Baddeley, Rubak, and Turner 2015) (p. 295).
The metrics shown before are an average over the entire window \(W\). Anselin (1995) proposed an alternative approach which is termed local indicators of spatial association (LISA). Instead of a global average LISA shows the local contributions of each point to the overall metric (Baddeley, Rubak, and Turner 2015) (p. 247). This is a general concept not only for point pattern analysis but for lattice data analysis as well (Anselin 1995, 2019).
# calculate LISA K curves
resLocal <- localK(ppSub, verbose=FALSE)
# code adapted from
# https://robinsonlabuzh.github.io/
# pasta/01-imaging-univar-ppSOD.html
df <- resLocal |>
as.data.frame() |>
pivot_longer(
iso0001:iso1327,
names_to="curve")
sel <- df |>
filter(r > 700.5630 & r < 702.4388) |>
mutate(sel=value) |>
select(curve, sel)
df <- left_join(df, sel)
thm <- list(
theme_light(),
theme(legend.position="none"),
scale_color_viridis_c())
p <- ggplot(df, aes(r, value, group=curve, col=sel)) +
geom_line() +
geom_line(aes(y=theo), linetype=2, col="darkgray") +
geom_vline(xintercept=700) +
thm
df <- data.frame(
x=ppSub$x, y=ppSub$y,
sel=unique(sel)$sel)
q <- ggplot(df, aes(x, y, col=sel)) +
coord_equal(expand=FALSE) +
geom_point(size=1) +
thm
p | q
The LISA Ripley’s \(K\) are colored by their value at \(r = 700\). We note that the LISA Ripley’s \(K\) gives two populations of curves. Those curves that increase at radii \(r<500 µm\) above the CSR line indicated in gray and the other curves that remain either below the gray CSR line or increase after \(r>500µm\).
If the values at \(r=700\) of the curves are projected back into the physical space, we note that the curves above the gray CSR line are the highly clustered cells in the bottom left. The other curves are the more spaced cells.