31 Imputation

31.1 Preamble

31.1.1 Introduction

As was previously discussed, most commonly employed imaging-based ST assays can resolve 100s to 1000s features. Meanwhile, non-spatially resolved single-cell assays are able to capture transcripts across 10,000 gene features. To allow estimating the expression of genes that may not be available in a ST assay, we would like to integrate spatially resolved data with scRNA-seq data using common features, in order to predict missing features using nearest neighbors (i.e., transcriptionally similar) cells.

31.1.2 Dependencies

Code

library(DropletUtils)
library(ggspavis)
library(harmony)
library(igraph)
library(OSTA.data)
library(patchwork)
library(RANN)
library(scran)
library(scater)
library(SpatialExperiment)
library(SpatialExperimentIO)

31.2 Data import

The analyses demonstrated here will use 313-plex Xenium data (10x Genomics) of a human breast cancer biopsy section and the Chromium Single Cell Gene Expression Flex applied to FFPE tissues (scFFPE-Seq) assay with around 18,000 features (Janesick et al. 2023).

31.2.1 Xenium

Code

id <- "Xenium_HumanBreast1_Janesick"
pa <- OSTA.data_load(id)
dir.create(td <- tempfile())
unzip(pa, exdir=td)
spe <- readXeniumSXE(td)
spe$sample_id <- "Xenium"
spe

##  class: SpatialExperiment 
##  dim: 313 167780 
##  metadata(4): experiment.xenium transcripts cell_boundaries
##    nucleus_boundaries
##  assays(1): counts
##  rownames(313): ABCC11 ACTA2 ... ZEB2 ZNF562
##  rowData names(3): ID Symbol Type
##  colnames(167780): 1 2 ... 167779 167780
##  colData names(8): cell_id transcript_counts ... nucleus_area
##    sample_id
##  reducedDimNames(0):
##  mainExpName: NULL
##  altExpNames(4): NegControlProbe NegControlCodeword antisense BLANK
##  spatialCoords names(2) : x_centroid y_centroid
##  imgData names(1): sample_id

31.2.2 Chromium

We now load the single cell FFPE RNA-Seq assay. To streamline the demonstration, we consolidate some of the cell type annotations provided by 10x Genomics (i.e., Annotation) into the scRNA-seq assay. The single-cell assay has 18,082 features, some of which are available in the Xenium assay as well.

Code

id <- "Chromium_HumanBreast_Janesick"
pa <- OSTA.data_load(id)
dir.create(td <- tempfile())
unzip(pa, exdir=td)
h5 <- file.path(td, "filtered_feature_bc_matrix.h5")
sce <- read10xCounts(h5, col.names=TRUE)
sce$sample_id <- "Chromium"
# set gene symbols as feature names
rownames(sce) <- make.unique(rowData(sce)$Symbol)
# retrieve cell type labels
fs <- list.files(td, full.names=TRUE)
csv <- grep("csv$", fs, value=TRUE)
cd <- read.csv(csv, row.names=1)
colData(sce)[names(cd)] <- cd[colnames(sce), ]
sce

##  class: SingleCellExperiment 
##  dim: 18082 30365 
##  metadata(1): Samples
##  assays(1): counts
##  rownames(18082): SAMD11 NOC2L ... MT-ND6 MT-CYB
##  rowData names(3): ID Symbol Type
##  colnames(30365): AAACAAGCAAACGGGA-1 AAACAAGCAAATAGGA-1 ...
##    TTTGTGAGTTGTCATA-1 TTTGTGAGTTTGGCCA-1
##  colData names(4): Sample Barcode sample_id Annotation
##  reducedDimNames(0):
##  mainExpName: NULL
##  altExpNames(0):

31.3 Analysis

31.3.1 Integration

To accomplish data transfer across cells of either two assays (scRNA-seq and imaging-based ST), we first need to find cells that are similar between the two datasets. To this end, we use the common genes of both assays to integrate both datasets into a joint embedding space. Here, the objective is to find the nearest scRNA-seq cell neighbors of each Xenium cell in the joint embedding space, assuming that these cells will have similar transcriptional profiles.

We can integrate them on a transcriptional level; here, using harmony. To this end, we first consolidate the scRNA-seq and Xenium data into one object:

Code

# concatenate objects
gs <- intersect(rownames(spe), rownames(sce))
cd <- intersect(names(colData(spe)), names(colData(sce)))
obj <- lapply(list(spe, sce), \(obj) {
    obj <- obj[gs, ]
    y <- as(assay(obj), "dgCMatrix")
    SingleCellExperiment(
        assays=list(counts=y),
        colData=colData(obj)[cd])
}) |> do.call(what=cbind)
# add single-cell annotations
lab <- match(colnames(obj), colnames(sce))
obj$Annotation <- sce$Annotation[lab]

We now filter out cells with too few counts, and then perform PC-based harmony integration:

Code

# minimal filtering
obj <- obj[, colSums(counts(obj)) > 20]
# log-library size normalization 
obj <- logNormCounts(obj)
# principal component analysis
# (w/o additional feature selection)
obj <- runPCA(obj, name=".PCA")
# 'harmony' integration
# (keeping uncorrected PCs)
pcs <- RunHarmony(
    data_mat=reducedDim(obj, ".PCA"), 
    meta_data=obj$sample_id, 
    verbose=FALSE)

##  Warning: Quick-TRANSfer stage steps exceeded maximum (= 9455000)
##  Warning: Quick-TRANSfer stage steps exceeded maximum (= 9455000)
##  Warning: Quick-TRANSfer stage steps exceeded maximum (= 9455000)

##  Warning: did not converge in 25 iterations

##  Warning: Quick-TRANSfer stage steps exceeded maximum (= 9455000)
##  Warning: Quick-TRANSfer stage steps exceeded maximum (= 9455000)
##  Warning: Quick-TRANSfer stage steps exceeded maximum (= 9455000)

Code

reducedDim(obj, "PCA") <- pcs
# dimensionality reduction before/after 
# integration (for visualization only)
obj <- runUMAP(obj, dimred=".PCA", name=".UMAP")
obj <- runUMAP(obj, dimred="PCA", name="UMAP")

Let us now visualize the embedding of both Xenium and scRNA-seq cells to assess the mixing of observations from both datasets. We also check if scRNA-seq cells are well-partitioned given their annotations:

Code

.obj <- obj[, obj$sample_id == "Chromium"]
plotUMAP(obj, colour_by="sample_id", point_size=0, dimred=".UMAP") + ggtitle("uncorrected") +
plotUMAP(obj, colour_by="sample_id", point_size=0) + ggtitle("corrected") +
plotUMAP(.obj, colour_by="Annotation", point_size=0) + ggtitle("Chromium") +
plot_layout(nrow=1, guides="collect") &
    guides(col=guide_legend(override.aes=list(alpha=1, size=2))) &
    theme_void() & theme(
        aspect.ratio=1, 
        legend.key.size=unit(0, "pt"),
        plot.title=element_text(hjust=0.5))

31.3.2 Selection

Here, we choose a small subset of features to transfer onto the Xenium assay. For this purpose, we test for differentially expressed genes (DEGs) between cell types (Annotation column), and select the top-20 markers for each:

Code

# normalize
sce <- logNormCounts(sce)
# test for differentially expressed genes 
# (DEGs) to identify subpopulation markers
colLabels(sce) <- sce$Annotation
mgs <- findMarkers(sce, test="wilcox", direction="up")
# get top markers per cell type
top <- lapply(names(mgs), \(k) {
    df <- mgs[[k]][, c("FDR", "summary.AUC")]
    g <- head(rownames(df), 20)
    data.frame(k, g, df[g, ])
}) |> do.call(what=rbind)
rownames(top) <- NULL
head(top)

##          k     g           FDR summary.AUC
##  1 B Cells  CD74  0.000000e+00   0.9477608
##  2 B Cells MS4A1  0.000000e+00   0.8773396
##  3 B Cells CXCR4 9.594950e-304   0.8271647
##  4 B Cells SP140 4.172406e-229   0.7843702
##  5 B Cells BANK1 1.321886e-228   0.7840307
##  6 B Cells CIITA  0.000000e+00   0.8615623

31.3.3 Aggregation

Now that we have shown there is a correction for batch effects between Xenium and Chromium assays, we can transfer counts from single-cell onto Xenium data by identifying the k-nearest neighbors (kNNs) of each Xenium cell in the Chromium data, and imputing missing genes by aggregating their profile across kNNs (here, using mean expression).

Code

# find k-nearest neighbors across embeddings
# (from Xenium to Chromium cells)
idx <- split(colnames(obj), obj$sample_id)
pcs <- reducedDim(obj, "PCA")
ref <- pcs[idx$Chromium, ]
que <- pcs[idx$Xenium, ]
knn <- nn2(data=ref, query=que, k=k <- 20)
# create adjacency matrix
el <- cbind(
    rep(idx$Xenium, each=k), 
    idx$Chromium[c(t(knn$nn.idx))])
g <- graph_from_edgelist(el, directed=TRUE)
A <- as_adjacency_matrix(g)
# average Chromium across Xenium neighbors
gs <- unique(top$g)
cs <- intersect(rownames(A), idx$Chromium)
ws <- A[idx$Xenium, cs]*(1/k)
y <- logcounts(sce)[gs, cs] %*% t(ws)

31.4 Downstream

31.4.1 Visualization

We now create two new SpatialExperiment objects by i) subsetting cells that passed our minimal filtering, and ii) replacing observed by imputed counts in one object:

Code

spe <- spe[, idx$Xenium]
spe <- logNormCounts(spe)
sqe <- SpatialExperiment(
    assays=list(logcounts=y), 
    spatialCoords=spatialCoords(spe))
# needed for 'ggspavis' visualization
spe$in_tissue <- sqe$in_tissue <- 1

We can now compare imputed to observed counts. Here, we visualize ACTA2, which is included in both assays, as well as KRT17, which also marks myoepithelial cells:

Code

.p <- \(obj, col) {
    plotSpots(obj, 
        point_size=0,
        annotate=col, 
        assay_name="logcounts")
}
pal <- scale_color_gradient2("obs.", high="red")
qal <- scale_color_gradient2("imp.", high="blue")
.p(spe, "ACTA2") + pal +
.p(sqe, "ACTA2") + qal +
.p(sqe, "KRT17") + qal +
    plot_layout(nrow=1)

31.4.2 Comparison

As a more systematic comparison, we can visualize the correlation of cell-wise expression values (using shared features only) between observed and imputed data:

Code

gs <- intersect(rownames(spe), rownames(sqe))
imp <- as.matrix(t(logcounts(sqe[gs, ])))
obs <- as.matrix(t(logcounts(spe[gs, ])))
cm <- cor(imp, obs, method="pearson")
df <- data.frame(g=gs, p=diag(cm))
df$g <- factor(df$g, df$g[order(-df$p)])
ggplot(df, aes(g, p, fill=p > 0.5)) + geom_col() +
    labs(x="correlation (observed vs. imputed)", y=NULL) +
    geom_hline(yintercept=0.5) +
    theme_minimal() + theme(
        legend.position="none",
        panel.grid=element_blank(),
        axis.text.x=element_text(angle=90, vjust=0.5, hjust=1))

We observe the highest correlation between observed and imputed data for KRT7; let’s confirm this by visualizing the gene’s expression in space:

Code

.p(spe, "KRT7") + pal +
.p(sqe, "KRT7") + qal +
    plot_layout(nrow=1)

Genes that exhibit the lowest correlation tend to be rarely expressed. This arguably makes it difficult to identify similar Chromium cells and, in turn, accurately transfer information between both assays. By contrast, highly correlated genes are detected in a decent proportion of cells:

Code

# quantify proportion of cells for which
# lowly/highly correlated genes are detected
fq <- \(gs) {
    y <- counts(spe[gs, ])
    round(rowMeans(y > 0), 2)
}
gs <- names(sort(diag(cm)))
fq(tail(gs)) # high corr.

##     FASN TACSTD2   ERBB2   CXCR4    IL7R    KRT7 
##     0.53    0.57    0.89    0.49    0.26    0.68

Code

fq(head(gs)) # low corr.

##   HDC CTSG CPA3 GZMB  KIT CD83 
##  0.30 0.01 0.03 0.07 0.06 0.08

31.5 Other methods

Alternatively, we can use other R/Python frameworks to integrate imaging-based ST assays with scRNA-seq datasets, and impute missing features. Some of these methods could be used through R using packages such as reticulate and basilisk; see Chapter 3.

Tangram: is available as a Python module through scvi-tools (Biancalani et al. 2021). The tool performs a softmax optimization to generate a probabilistic mapping between cells of scRNA-seq data and voxels (cells or spots) of ST data, which is then used to transfer labels or feature profiles across datasets.
LIGER: is available in R via rliger (Welch et al. 2019), and incorporates a similar workflow performed in this chapter using harmony. The only difference between these approaches is how the joint embedding is obtained; here, LIGER uses non-negative matrix factorization (NMF). LIGER has been originally proposed to perform this task for scRNA-seq and scATAC-seq assays.

References

Biancalani, Tommaso, Gabriele Scalia, Lorenzo Buffoni, Raghav Avasthi, Ziqing Lu, Aman Sanger, Neriman Tokcan, et al. 2021. “Deep Learning and Alignment of Spatially Resolved Single-Cell Transcriptomes with Tangram.” Nature Methods 18 (11): 1352–62.

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 (1): 8353.

Welch, Joshua D, Velina Kozareva, Ashley Ferreira, Charles Vanderburg, Carly Martin, and Evan Z Macosko. 2019. “Single-Cell Multi-Omic Integration Compares and Contrasts Features of Brain Cell Identity.” Cell 177 (7): 1873–87.