18  Intermediate processing

18.1 Preamble

18.1.1 Introduction

In this demo, we will continue on processing Xenium data on a human breast cancer biopsy section from Janesick et al. (2023), which has already undergone quality control and minimal filtering; see Chapter 17. The processing steps carried out here – namely, normalization, dimension reduction, and clustering – lay the foundating for a variety of downstream analysis tasks that will be covered in the next chapters. Here, we will run standard non-spatial and spatially aware approaches; for a more comprehensive overview of methodology and tools, see Chapter 22 on dimension reduction and Chapter 23 on clustering.

18.1.2 Dependencies

Code
library(scran)
library(scater)
library(igraph)
library(Banksy)
library(ggplot2)
library(ggspavis)
library(patchwork)
library(OSTA.data)
library(SpatialExperiment)
# set seed for random number generation
# in order to make results reproducible
set.seed(20000229)
# load data from preceding 
# chapter (post quality control)
(spe <- readRDS("img-spe_qc.rds"))
##  class: SpatialExperiment 
##  dim: 313 140268 
##  metadata(0):
##  assays(1): counts
##  rownames(313): ABCC11 ACTA2 ... ZEB2 ZNF562
##  rowData names(3): ID Symbol Type
##  colnames(140268): 2 3 ... 167779 167780
##  colData names(11): cell_id transcript_counts ... detected total
##  reducedDimNames(0):
##  mainExpName: NULL
##  altExpNames(0):
##  spatialCoords names(2) : x_centroid y_centroid
##  imgData names(1): sample_id

Before getting started, we will retrieve the authors’ cell type labels, which were obtained by transferring scFFPE-seq annotations (supervised); these comprise 20 subpopulations.

Code
# 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]

18.2 Normalization

Library size-based normalization as is typical for scRNA-seq data has been shown to be problematic for ST data, especially so for targeted panels underlying current commercial imaging-based ST platforms (Atta et al. 2024; Bhuva et al. 2024). For lack of a better approach, we here use standard log-library size normalization. We caution readers, however, to keep an eye out in the literature for attempts to provide a better strategy.

Code
spe <- logNormCounts(spe)

18.3 Feature selection

At this stage, we would typically perform selection of (e.g., highly variable) features; see Chapter 10. The dataset at hand, however, is targeted and relatively low-plex, so that features have already been selected by design (e.g., different targets will be included in immuno-oncology and neuroscience panels).

18.4 Dimension Reduction

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.

Code
spe <- runPCA(spe, ncomponents=20)

For comparison, we will also perform spatially-aware dimension reduction with BANKSY (Singhal et al. 2024); see Chapter 22.

Code
spe <- computeBanksy(spe, assay_name="logcounts")
spe <- runBanksyPCA(spe, npcs=20, lambda=0.2)

To not confuse different types of PCs, we rename reducedDims to end in _sp and _tx for spatially aware and unaware results, respectively.

Code
reducedDimNames(spe) <- c("PCA_tx", "PCA_sp")

18.5 Clustering

Here, we perform standard graph-based clustering by (i) constructing a shared nearest neighbor (SNN) graph and (ii) using the Leiden algorithm for community detection. By basing the SNN graph on standard and BANKSY PCs, respectively, we can obtain non-spatial as well as spatially aware assignments:

Code
pcs <- c(Leiden="PCA_tx", Banksy="PCA_sp")
for (. in names(pcs)) {
    # build cellular shared nearest-neighbor (SNN) graph
    g <- buildSNNGraph(spe, use.dimred=pcs[.], type="jaccard", k=20)
    # cluster using Leiden community detection algorithm
    k <- cluster_leiden(g, objective_function="modularity", resolution=0.8)
    spe[[.]] <- factor(k$membership)
}

18.6 Visualization

Let’s visualize the assignment obtains from non-spatial and spatially aware clustering:

Code
spe$in_tissue <- 1; spe$x_centroid <- spe$y_centroid <- NULL
lapply(c("Label", "Leiden", "Banksy"), \(.) {
  plotCoords(spe, annotate=., point_size = 0.1)
}) |>
    wrap_plots(nrow=1) &
    theme(legend.key.size=unit(0, "lines")) &
    scale_color_manual(values=unname(pals::trubetskoy()))

18.7 Appendix

Save data

Code
colLabels(spe) <- spe$Banksy
saveRDS(spe, "img-spe_cl.rds")

References

Atta, Lyla, Kalen Clifton, Manjari Anant, Gohta Aihara, and Jean Fan. 2024. “Gene Count Normalization in Single-Cell Imaging-Based Spatially Resolved Transcriptomics.” Genome Biology 25 (153). https://doi.org/10.1186/s13059-024-03303-w.
Bhuva, Dharmesh D., Chin Wee Tan, Agus Salim, Claire Marceaux, Marie A. Pickering, Jinjin Chen, Malvika Kharbanda, et al. 2024. “Library Size Confounds Biology in Spatial Transcriptomics Data.” Genome Biology 25 (99). https://doi.org/10.1186/s13059-024-03241-7.
Janesick, Amanda, Robert Shelansky, Andrew D. Gottscho, Florian Wagner, Stephen R. Williams, Morgane Rouault, Ghezal Beliakoff, et al. 2023. “High Resolution Mapping of the Tumor Microenvironment Using Integrated Single-Cell, Spatial and in Situ Analysis.” Nature Communications 14 (8353). https://doi.org/10.1038/s41467-023-43458-x.
Singhal, Vipul, Nigel Chou, Joseph Lee, Yifei Yue, Jinyue Liu, Wan Kee Chock, Li Lin, et al. 2024. “BANKSY Unifies Cell Typing and Tissue Domain Segmentation for Scalable Spatial Omics Data Analysis.” Nature Genetics 56: 431–41. https://doi.org/10.1038/s41588-024-01664-3.
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