In single-cell omics data analysis, dimension reduction (DR) techniques are often categorized as linear (e.g., multi-dimensional scaling (MDS), linear discriminant analysis (LDA), principal component analysis (PCA)), or non-linear (e.g., t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP)); see OSCA.
In the context of ST data, we would instead like to distinguish between spatially aware and non-spatial methods, i.e., whether or not DR incorporates physical locations or is based on molecular profiles only. Spatially aware DR may be used as input for standard single-cell clustering approaches (say, SNN-graph based Leiden), while non-spatial DR may be used for spatially aware clustering. While these approach may differ in methodological detail, they are arguably similar conceptually. We will try and demonstrate as much here.
library(dplyr)
library(Banksy)
library(scater)
library(ggplot2)
library(OSTA.data)
library(patchwork)
library(SpatialExperiment)
# set seed for random number generation
# in order to make results reproducible
set.seed(20000229)
# load Xenium data post quality control
spe <- readRDS("img-spe_qc.rds")# get annotations from 'BiocFileCache'
# (data has been retrieved already)
id <- "Xenium_HumanBreast1_Janesick"
pa <- OSTA.data_load(id)
dir.create(td <- tempfile())
unzip(pa, "annotation.csv", exdir=td)
df <- read.csv(list.files(td, full.names=TRUE))
# add annotations as cell metadata
cs <- match(spe$cell_id, df$Barcode)
spe$Label <- df$Annotation[cs]As a baseline, we will perform principal component analysis (PCA), which underlies many standard scRNA-seq analysis pipelines, such as (spatially unaware) graph-based clustering based on a shared nearest neighbor (SNN) graph and the Leiden or Louvain algorithm for community detection.
A standard approach is to apply PCA to the set of top HVGs, and retain a subset of PCs for subsequent steps. This is done for two main reasons: (i) to reduce noise due to random variation in expression of biologically uninformative genes, which are assumed to have expression patterns independent of each other, and (ii) to improve computational efficiency. Because our data are relatively low-plex in this example, we use all features instead.
Here, we use the implementation of PCA provided in the scater package (McCarthy et al. 2017):
BSPARAM=RandomParam() can be used to decrease runtime by approximating the singular value decomposition (SVD). Because this implementation uses randomization, and seed for random number generation should be set in order to make results reproducible.spe <- logNormCounts(spe)
spe <- runPCA(spe, ncomponents=20)BANKSY (Singhal et al. 2024) computes PCs on a spatial-neighborhood augmented matrix, thereby embedding cells in a product space of their own and their local neighborhood’s (average) transcriptome, representing cell state and microenvironment. A key parameter for this method is \(\lambda\in[0,1]\) (argument lambda in runBanksyPCA()), which controls the spatial component’s weight; notably, when \(\lambda=0\), BANKSY reduces to non-spatial clustering. Secondly, k_geom determines the number of neighbors to use for computing local transcriptomic neighborhoods.
spe <- computeBanksy(spe, assay_name="logcounts", k_geom=20)
spe <- runBanksyPCA(spe, npcs=20, lambda=0.4)To not confuse different types of PCs, we rename the corresponding reducedDims to end in _sp and _tx for spatially aware and unaware results, respectively.
reducedDimNames(spe) <- c("PCA_tx", "PCA_sp")The UMAP algorithm is often used for visualization purposes (although interpretation of the UMAP dimensions is difficult and can be misleading).
Here, we run UMAP on both non-spatial and spatially aware PCs, retaining the first two UMAP components to be used for visualization:
A useful visualization is to color cells by their PCs in physical space. This will help highlight key drivers of transcriptional variability, e.g., between major biological compartments such as epithelia, immune and stromal cells. Exemplary plots of PCs 1-3 are rendered below:
df <- data.frame(colData(spe), reducedDim(spe, "PCA_tx"))
lapply(paste0("PC", seq(3)), \(.) {
fd <- df[order(abs(df[[.]])), ]
ggplot(fd, aes(x_centroid, y_centroid, col=.data[[.]]))
}) |>
wrap_plots(nrow=1) &
geom_point(shape=16, stroke=0, size=0.2) &
scale_color_gradientn(colors=pals::jet()) &
coord_equal() & theme_void() & theme(
legend.position="bottom",
legend.key.width=unit(0.8, "lines"),
legend.key.height=unit(0.4, "lines"))
A neat trick in this context is to rescale PC 1-3 values between 0 and 1, and interpret them as RGB values for visualization:
pcs <- reducedDim(spe, "PCA_tx")[, seq(3)]
pcs <- sweep(pcs, 2, colMins(pcs), `-`)
pcs <- sweep(pcs, 2, colMaxs(pcs), `/`)
rgb <- apply(pcs, 1, \(.) rgb(.[1], .[2], .[3]))
df <- data.frame(colData(spe), rgb)E.g., we see that colors corresponding to opposing PC values are clearly separated in space, indicating distinct drivers of transcriptional variability. In some regions, we see color mixtures, indicating cells of ambiguous transcriptional profiles (with respect to what is being captured by the 3 PCs considered here).
ggplot(df, aes(x_centroid, y_centroid, col=rgb)) +
geom_point(shape=16, stroke=0, size=0.4) +
scale_color_identity() + coord_equal() + theme_void()
.plot_umap <- \(dr) plotReducedDim(
object=spe, dimred=dr, colour_by="Label",
point_shape=16, point_size=0) + ggtitle(dr)
.plot_umap("UMAP_tx") +
.plot_umap("UMAP_sp") +
plot_layout(guides="collect") &
guides(col=guide_legend(override.aes=list(alpha=1, size=2))) &
theme_void() & theme(aspect.ratio=1,
legend.key.size=unit(0, "lines"),
plot.title=element_text(hjust=0.5))