Visium HD is a next-generation spatial transcriptomics technology developed by 10x Genomics, designed to provide single-cell resolution spatial gene expression data across entire tissue sections. Commercially available since 2024, Visium HD advanced the platform’s resolution from 55 µm spots to bins of near subcellular resolution (2 µm). In this workflow, we will demonstrate common analysis steps with a Visium HD dataset on colorectal cancer (Oliveira et al. 2024).
library(Banksy)
library(BiocParallel)
library(DropletUtils)
library(ggplot2)
library(ggspavis)
library(igraph)
library(OSTA.data)
library(patchwork)
library(pheatmap)
library(scater)
library(scran)
library(scuttle)
library(spacexr)
library(SpatialExperiment)
library(SpotSweeper)
library(Statial)
library(tidyr)
library(VisiumIO)In this demo, we perform deconvolution on 16 µm bins and compare the concordance with results on 8 µm bins, provided by 10x Genomics.
# retrieve dataset from OSF repo
id <- "VisiumHD_HumanColon_Oliveira"
pa <- OSTA.data_load(id)
dir.create(td <- tempfile())
unzip(pa, exdir=td)
# read 8um bins into 'SpatialExperiment'
vhd8 <- TENxVisiumHD(
spacerangerOut=td,
processing="filtered",
format="h5",
images="lowres",
bin_size="008") |>
import()
# use gene symbols as feature names
gs <- rowData(vhd8)$Symbol
rownames(vhd8) <- make.unique(gs)
colnames(vhd8) <- vhd8$barcode # TODO: VisiumIO bug?
vhd8## class: SpatialExperiment
## dim: 18085 545913
## metadata(2): resources spatialList
## assays(1): counts
## rownames(18085): SAMD11 NOC2L ... MT-ND6 MT-CYB
## rowData names(3): ID Symbol Type
## colnames(545913): s_008um_00301_00321-1 s_008um_00526_00291-1 ...
## s_008um_00353_00477-1 s_008um_00595_00611-1
## colData names(6): barcode in_tissue ... bin_size sample_id
## reducedDimNames(0):
## mainExpName: Gene Expression
## altExpNames(0):
## spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
## imgData names(4): sample_id image_id data scaleFactorThese data come with bin-level annotations from deconvolution estimates by RCTD, implemented in spacexr. Specifically, two sets of labels are available that correspond to the most and second most frequent of 38 cell types per bin, respectively:
# retrieve annotations
gz <- "binned_outputs/square_008um/deconvolution.csv.gz"
df <- read.csv(file.path(td, gz), row.names=2)
head(df <- df[complete.cases(df), -1])## DeconClass DeconLabel1 DeconLabel2
## s_008um_00000_00001-1 singlet Pericytes Endothelial
## s_008um_00000_00017-1 singlet Enteric Glial Enterocyte
## s_008um_00000_00018-1 doublet_uncertain Enteric Glial Tumor II
## s_008um_00000_00019-1 singlet vSM Tuft
## s_008um_00000_00020-1 singlet vSM CD8 Cytotoxic T cell
## s_008um_00000_00023-1 reject Smooth Muscle vSMlab <- grep("Label", names(cd <- colData(vhd8)), value=TRUE)
pal <- hcl.colors(length(unique(unlist(cd[lab]))), "Spectral")
lapply(lab, \(.) {
p <- plotSpots(vhd8, annotate=., point_size=0.2) + ggtitle(.)
p$layers[[1]]$aes_params$shape <- 16
p$layers[[1]]$aes_params$stroke <- 0
p
}) |>
wrap_plots(nrow=1, guides="collect") &
scale_color_manual(NULL, values=pal) &
theme(legend.key.size=unit(0, "lines"))
Another resolution of VisiumHD with 16 µm bin can be read in as follow:
# read 16um bins into 'SpatialExperiment'
vhd16 <- TENxVisiumHD(
spacerangerOut=td,
processing="filtered",
format="h5",
images="lowres",
bin_size="016") |>
import()
# use symbols as feature names
gs <- rowData(vhd16)$Symbol
rownames(vhd16) <- make.unique(gs)
colnames(vhd16) <- vhd16$barcode # TODO: VisiumIO bug?
vhd16## class: SpatialExperiment
## dim: 18085 137051
## metadata(2): resources spatialList
## assays(1): counts
## rownames(18085): SAMD11 NOC2L ... MT-ND6 MT-CYB
## rowData names(3): ID Symbol Type
## colnames(137051): s_016um_00052_00082-1 s_016um_00010_00367-1 ...
## s_016um_00037_00193-1 s_016um_00144_00329-1
## colData names(6): barcode in_tissue ... bin_size sample_id
## reducedDimNames(0):
## mainExpName: Gene Expression
## altExpNames(0):
## spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
## imgData names(4): sample_id image_id data scaleFactorSome global filtering based on library size is necessary to remove bins that overlay empty tissue.
We can visualize such bins by zooming into an empty tissue region. On the left-hand side, one region shows a clear gap with low counts. Informed by H&E staining, bins located above tissue gaps should not be interpreted biologically, as the low counts in these bins are likely due to ambient RNA or transcript spillover. Therefore, they should be excluded from downstream analysis.
Based on a discussion with the original author at 10x Genomics, in this dataset, 8 µm bins with a library size below 100 are removed. Since we expect a 4-fold increase in library size across all bins at 16 µm, we can set the filtering threshold to 400.
p1 <- plotVisium(vhd16, annotate="libsize",
zoom = TRUE, show_axes = TRUE, point_size = 1.3) +
xlim(c(422, 442)) + ylim(c(318, 330)) +
facet_null() + ggtitle("16 µm - original")
vhd16 <- vhd16[, vhd16$libsize > 400]
p2 <- plotVisium(vhd16, annotate="libsize",
zoom = TRUE, show_axes = TRUE, point_size = 1.3) +
xlim(c(422, 442)) + ylim(c(318, 330)) +
facet_null() + ggtitle("16 µm - after > 400 UMI QC")
p1 | p2
Clustering results generated by Space Ranger are provided for both resolutions.
We can visualize the given clustering results at 8 µm and 16 µm resolutions. Later, we will compare the clustering result at 16 µm with that from Banksy.
To keep runtimes low, our downstream analysis will be performed on a subset area of 16 µm bins (1/64 of the data coverage area), which we read in here; there are no annotations available for this resolution in the public domain.
.rng <- \(spe) {
xy <- spatialCoords(spe)*scaleFactors(spe)
xs <- range(xy[, 1]); ys <- nrow(imgRaster(spe))-range(xy[, 2])
x1 <- xs[1] + 4*(xs[2]-xs[1])/8; x2 <- xs[2] - 3*(xs[2]-xs[1])/8
y1 <- ys[1] + 3*(ys[2]-ys[1])/8; y2 <- ys[2] - 4*(ys[2]-ys[1])/8
list(box=c(x1, x2, y1, y2), cov=c(xs, ys))
}
vhd8r <- .rng(vhd8)
# vhd16r <- .rng(vhd16)
# use 8um bounding boxes to subset spes at both resolutions;
# it is similar to 16um's box range
.box <- \(roi, lty, lwd, col) {
geom_rect(
xmin=vhd8r[[roi]][1], xmax=vhd8r[[roi]][2],
ymin=vhd8r[[roi]][3], ymax=vhd8r[[roi]][4],
col=col, fill=NA, linetype=lty, linewidth=lwd)
}
cov <- .box(roi="cov", lty=2, lwd=1/2, col="grey")
box <- .box(roi="box", lty=4, lwd=2/3, col="black")
# plotting
.lim <- \(spe) list(
xlim(spe[["box"]][c(1, 2)]),
ylim(spe[["box"]][c(4, 3)]))
plotVisium(vhd8, spots=FALSE) + cov + box +
ggtitle("grey: data coverage\n black: zoomed region") +
plotVisium(vhd8, point_size=0.8, zoom=TRUE) + .lim(vhd8r) +
ggtitle("8 µm bins in black box") +
plotVisium(vhd16, point_size=1.6, zoom=TRUE) + .lim(vhd8r) +
ggtitle("16 µm bins in black box") +
plot_layout(nrow=1, widths=c(1.5, 1, 1)) & facet_null() &
theme(plot.title=element_text(hjust=0.5, vjust=0.5))
Subset the 16 µm object to bins in the black box:
.sub <- function(spe, rng, roi="box") {
xs <- rng[[roi]][c(1, 2)]
ys <- nrow(imgRaster(spe)) - rng[[roi]][c(3,4)]
xy <- spatialCoords(spe)*scaleFactors(spe)
spe[, xy[, 1] > xs[1] & xy[, 1] < xs[2] &
xy[, 2] > ys[1] & xy[, 2] < ys[2] ]
}
.vhd16 <- .sub(spe=vhd16, rng=vhd8r)
dim(.vhd16)## [1] 18085 2416From now on, we are operating on 2416 16 µm bins.
As detailed in ?sec-seq-quality-control, we use SpotSweeper to perform quality control on the subsetted 16 µm data.
# calculate per-cell QC metrics
mt <- grepl("^MT-", rownames(.vhd16))
.vhd16 <- addPerCellQCMetrics(.vhd16, subsets=list(mt=mt))
# determine outliers based on
# - low log-library size
# - few uniquely detected features
# - high mitochondrial count fraction
.vhd16 <- localOutliers(.vhd16, metric="sum", direction="lower", log=TRUE)
.vhd16 <- localOutliers(.vhd16, metric="detected", direction="lower", log=TRUE)
.vhd16 <- localOutliers(.vhd16, metric="subsets_mt_percent", direction="higher", log=TRUE)
.vhd16$discard <-
.vhd16$sum_outliers |
.vhd16$detected_outliers |
.vhd16$subsets_mt_percent_outliers
# tabulate number of bins retained
# vs. removed by any criterion
table(.vhd16$discard)##
## FALSE TRUE
## 2399 17
We can visualize the local outliers identified by SpotSweeper in space:
plotSpots(.vhd16, annotate="sum_outliers") + ggtitle("low_lib_size") +
plotSpots(.vhd16, annotate="detected_outliers") + ggtitle("low_n_features") +
plotSpots(.vhd16, annotate="discard") + ggtitle("discard") +
plot_layout(nrow=1, guides="collect") & theme(
plot.title=element_text(hjust=0.5),
legend.key.size=unit(0, "lines")) &
guides(col=guide_legend(override.aes=list(size=3))) &
scale_color_manual("discard", values=c("lavender", "purple"))
Lastly, we subset the Visium HD 16 µm object to bins that passed QC:
.vhd16 <- .vhd16[, !.vhd16$discard]
dim(.vhd16)## [1] 18085 2399First, we identify highly variable genes (HVGs) in the target area:
.vhd16 <- logNormCounts(.vhd16)
dec <- modelGeneVar(.vhd16)
hvg <- getTopHVGs(dec, n=3e3)As detailed in Chapter 24, Banksy utilizes a pair of spatial kernels to capture gene expression variation, followed by dimension reduction and graph-based clustering to identify spatial domains.
# set seed for random number generation
# in order to make results reproducible
set.seed(112358)
# 'Banksy' parameter settings
k <- 8 # consider first order neighbors
l <- 0.2 # use little spatial information
a <- "logcounts"
xy <- c("array_row", "array_col")
# restrict to selected features
tmp <- .vhd16[hvg, ]
# compute spatially aware 'Banksy' PCs
tmp <- computeBanksy(tmp, assay_name=a, coord_names=xy, k_geom=k)
tmp <- runBanksyPCA(tmp, lambda=l, npcs=20)
reducedDim(.vhd16, "PCA") <- reducedDim(tmp)
## run UMAP (for visualization purposes only)
# .vhd16 <- runUMAP(.vhd16, dimred="PCA")
# build cellular shared nearest-neighbor (SNN) graph
g <- buildSNNGraph(.vhd16, use.dimred="PCA", type="jaccard", k=20)
# cluster using Leiden community detection algorithm
k <- cluster_leiden(g, objective_function="modularity", resolution=1.2)
table(.vhd16$Banksy <- factor(k$membership))##
## 1 2 3 4 5 6 7 8 9 10 11 12 13
## 153 184 317 186 228 181 215 152 178 113 147 163 182Next, we can perform differential gene expresison (DGE) analysis to identify markers for each cluster; c.f. Chapter 25. We also compute the cluster-wise mean expression of selected markers:
# differential gene expression analysis
mgs <- findMarkers(.vhd16, groups=.vhd16$Banksy, direction="up")
# select for a few markers per cluster
top <- lapply(mgs, \(df) rownames(df)[df$Top <= 3])
top <- unique(unlist(top))
# average expression by clusters
pbs <- aggregateAcrossCells(.vhd16,
ids=.vhd16$Banksy, subset.row=top,
use.assay.type="logcounts", statistics="mean")The marker genes in each cluster can be visualized with a heatmap:
Or, we can visualize the bin-wise expression of selected markers in space:
gs <- c("MMP2", "PIGR", "IGHG1")
ps <- lapply(gs, \(.) plotSpots(.vhd16, annotate=., assay_name="logcounts"))
wrap_plots(ps, nrow=1) & theme(
legend.key.width=unit(0.5, "lines"),
legend.key.height=unit(1, "lines")) &
scale_color_gradientn(colors=rev(hcl.colors(9, "Rocket")))
We will now perform deconvolution on the 16 µm binned Visium HD data. First, we retrieve matching (Chromium) scRNA-seq data, which includes low- (Level1) and high-resolution (Level2) annotations of cells into 9 respectively 31 clusters. In order to ensure similar transcriptional profile across technologies, we filter the reference population to patient 2 only.
# retrieve dataset from OSF repository
id <- "Chromium_HumanColon_Oliveira"
pa <- OSTA.data_load(id)
dir.create(td <- tempfile())
unzip(pa, exdir=td)
# read into `SingleCellExperiment`
fs <- list.files(td, full.names=TRUE)
h5 <- grep("h5$", fs, value=TRUE)
sce <- read10xCounts(h5, col.names=TRUE)
# add cell metadata
csv <- grep("csv$", fs, value=TRUE)
cd <- read.csv(csv, row.names=1)
colData(sce)[names(cd)] <- cd[colnames(sce), ]
# use gene symbols as feature names
gs <- rowData(sce)$Symbol
rownames(sce) <- make.unique(gs)
# exclude cells deemed to be of low-quality
sce <- sce[, sce$QCFilter == "Keep"]
# subset cells from same patient
sce <- sce[, grepl("P2", sce$Patient)]
sce## class: SingleCellExperiment
## dim: 18082 67568
## metadata(1): Samples
## assays(1): counts
## rownames(18082): SAMD11 NOC2L ... MT-ND6 MT-CYB
## rowData names(3): ID Symbol Type
## colnames(67568): AAACAAGCAACAGCTAACTTTAGG-1
## AAACAAGCAACTGTTCACTTTAGG-1 ... TTTGTGAGTGCGTACCATGTTGAC-24
## TTTGTGAGTGGAAGCTATGTTGAC-24
## colData names(7): Sample Barcode ... Level1 Level2
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):We can visualize the mapping between low- and high-resolution annotations in the single-cell reference data with a contingency table:
fq <- prop.table(table(sce$Level1, sce$Level2), 2)
pheatmap(fq, cellwidth=10, cellheight=10, treeheight_row=5, treeheight_col=5)
Note that "Proliferating Immune II" spans evenly between "B cells" and "Tumor", so we will make it a separate class, "Prolif. Immune" in the low-resolution annotations:
##
## B cells Endothelial Fibroblast
## 6233 1969 7355
## Intestinal Epithelial Myeloid Neuronal
## 5949 6353 1216
## Prolif. Immune Smooth Muscle T cells
## 810 16428 6658
## Tumor
## 14597There are some differences in the provided deconvolution labels at 8 µm resolution and the high-resolution single-cell reference labels:
# all single-cell reference labels
# are present in the Visium HD data
setdiff(sce$Level2, vhd8$DeconLabel1)## character(0)setdiff(sce$Level2, vhd8$DeconLabel2)## character(0)# but not vice versa
setdiff(vhd8$DeconLabel1, sce$Level2)## [1] "Proliferating Macrophages" "Proliferating Fibroblast"
## [3] "Memory B" "Vascular Fibroblast"
## [5] "cDC I" "NK"
## [7] "Tumor IV"setdiff(vhd8$DeconLabel2, sce$Level2)## [1] "Proliferating Fibroblast" "Memory B"
## [3] "cDC I" "Proliferating Macrophages"
## [5] "Vascular Fibroblast" "Tumor IV"
## [7] "NK"Below, these extra classes in the Visium HD 8 μm bin annotations will be ignored.
We can merge the provided deconvolution annotation "DeconLabel1" (most likely cell type for singlets, most dominant type for doublets) into a lower resolution using the single-cell reference annotations:
##
## B cells Endothelial Fibroblast
## 12279 19978 75080
## Intestinal Epithelial Myeloid Neuronal
## 40683 20811 1200
## Prolif. Immune Smooth Muscle T cells
## 3660 15913 6158
## Tumor
## 215865According to RCTD, approximately 75% of the 8 µm bins are singlets:
Let us check the top-5 common doublet pairs according to the "doublet_certain" class:
dbl <- vhd8$DeconClass == "doublet_certain"
lab <- c(".DeconLabel1", ".DeconLabel2")
df <- data.frame(colData(vhd8)[dbl, lab])
# sort as to ignore order
df <- apply(df, 1, sort)
df <- do.call(rbind, df)
names(df) <- lab
# count unique pairs
ij <- paste(df[, 1], df[, 2], sep=";")
head(sort(table(ij), decreasing=TRUE), 5)## ij
## Myeloid;Tumor B cells;Fibroblast Fibroblast;Myeloid Tumor;Tumor
## 3799 3670 3453 2742
## Fibroblast;Tumor
## 2532We can see that myeloid are commonly co-localized with tumor and fibroblasts cells; most doublets are homotypic, i.e., they are (mostly) composed of one low-resolution type.
We can perform the same subsetting procedure to 8 µm bins and compare the difference in the number of bins in the zoomed (black box) area.
# subset zoomed (black box) area
.vhd8 <- .sub(spe=vhd8, rng=vhd8r)
# compare number of bins between resolutions
(n <- ncol(.vhd8))## [1] 8933(m <- ncol(.vhd16))## [1] 2399round(n/m, 2)## [1] 3.72As expected, the number of 8 µm bins is about 4 times the number of 16 µm bins. We will visualize the same subset for both bin sizes in any downstream analyses.
RCTD on 16 µm binsWe also assume there are at most two cell types in a 16 µm bin as in 8 µm, and thus we would expect a smaller proportion of singlets.
# downsample to at most 4,000 cells per cluster for sce
# (this is done only to keep runtime/memory low)
cs <- split(seq_len(ncol(sce)), sce$Level1)
cs <- lapply(cs, \(.) sample(., min(length(.), 4e3)))
ncol(.sce <- sce[, unlist(cs)])
rctd_data <- createRctd(.vhd16, .sce, cell_type_col="Level1")
(res <- runRctd(rctd_data, max_cores=4, rctd_mode="doublet"))Weights inferred by RCTD correspond to proportions of cell types, such that they sum to 1 for a given observation (expect for rejected ones, which sum to 0):
##
## FALSE TRUE
## 2388 11# add proportion estimates as metadata
ws <- data.frame(t(as.matrix(ws)))
colData(.vhd16)[names(ws)] <- ws[colnames(.vhd16), ]Next, we can spatially visualize the deconvolution proportions estimates:
lapply(names(ws), \(.)
plotSpots(.vhd16, annotate=., point_size=0.1)) |>
wrap_plots(nrow=2, guides="collect") & theme(
legend.key.width=unit(0.5, "lines"),
legend.key.height=unit(1, "lines")) &
scale_color_gradientn(colors=rev(hcl.colors(9, "Rocket")))
We may also obtain the majority vote class for 16 µm bins:
##
## B cells Endothelial Fibroblast
## 121 223 506
## Intestinal Epithelial Myeloid Neuronal
## 368 180 2
## Prolif. Immune Smooth Muscle T cells
## 77 60 49
## Tumor
## 813We can also check the proportional frequency difference between 8 and 16 µm bins in our region of interest:
n8 <- table(.vhd8$.DeconLabel1)
n16 <- table(.vhd16$.DeconLabel1)
df <- data.frame(x=as.numeric(n8/n16))
ggplot(df, aes(x)) + geom_density() +
xlab("Cell type frequency ratio") +
ggtitle("distribution of cell type frequency\nratio between 8 vs. 16 µm bins") +
scale_x_continuous(breaks=seq(0, max(df$x), by=2)) +
theme_classic() + theme(plot.title=element_text(hjust=0.5))
Most cell types appear around 3-4 times more in 8 compared to 16 µm bins, which is expected given the difference in bin size.
Let’s visualize deconvolution result for 8 and 16 µm bins in the zoomed area:
plotVisium(.vhd8, annotate=".DeconLabel1", zoom =TRUE, point_size=0.8) + ggtitle("8 µm") + plot_spacer() +
plotVisium(.vhd16, annotate=".DeconLabel1", zoom=TRUE, point_size=1.6) + ggtitle("16 µm") +
plot_layout(nrow=1, guides="collect", widths=c(1, 0.05, 1)) &
guides(col=guide_legend(override.aes=list(size=2))) &
scale_fill_manual(values=unname(pals::trubetskoy())) &
facet_null() & theme(
plot.title=element_text(hjust=0.5),
legend.key.size=unit(0, "lines")) 
We observe clear agreement in deconvolution at both 8 µm and 16 µm resolutions; however, the 8 µm resolution better captures fine structures, such as the string-like shape of endothelial and fibroblast bins extending into the upper left tumor region.
We can visualize the results spatially:
plotVisium(.vhd16, annotate="cluster", zoom=TRUE,
point_size=1.6, pal=unname(pals::kelly())) +
plot_spacer() +
plotVisium(.vhd16, annotate="Banksy", zoom=TRUE,
point_size=1.6, pal=unname(pals::kelly())) +
plot_spacer() +
plotVisium(.vhd16, annotate=".DeconLabel1", zoom=TRUE,
point_size=1.6, pal=unname(pals::trubetskoy())) +
plot_layout(nrow=1,
widths=c(1, 0.05, 1, 0.05, 1)) & facet_null() &
theme(legend.key.size=unit(0, "lines"))
We observe that Banksy delineates tissue borders very well, while deconvolution provides direct biological insight into the underlying clusters. Their concordance can be visualized using a heatmap:
fq <- prop.table(table(.vhd16$Banksy, .vhd16$.DeconLabel1), 1)
pheatmap(fq, cellwidth=10, cellheight=10, treeheight_row=5, treeheight_col=5)
From the above heatmap, cluster 5 and 7 are mostly immune cells; cluster 9 matches myeloid bins; cluster 13 and 1 are likely intestinal epithelial and smooth muscle, respectively; fibroblast is common in cluster 10; tumor spans cluster 3, 11, and 12.
Here, we want to quantify the abundance between T cells, B cells, or Myeloid bins to Fibroblast or Tumor bins in 8 µm zoomed region. We use getAbundances() from Statial to calculate, for each bin, the abundance of other cell types within a radius of 200 px; the resulting (bins \(\times\) types) matrix is stored as reducedDim slot "abundances".
.vhd8 <- .vhd8[, !is.na(.vhd8$.DeconLabel1)]
xy <- data.frame(spatialCoords(.vhd8))
colData(.vhd8)[names(xy)] <- xy
.vhd8 <- getAbundances(.vhd8,
cellType=".DeconLabel1",
imageID="sample_id",
spatialCoords=names(xy),
r=200, nCores=4)Let’s have a look at the abundance and distance results, also including deconvolution results:
df <- reducedDim(.vhd8, "abundances")
df$CT <- .vhd8$.DeconLabel1
df[1:5, 1:5]## Fibroblast T cells B cells Prolif. Immune Myeloid
## s_008um_00388_00417-1 38 0 3 0 1
## s_008um_00388_00418-1 43 0 3 1 0
## s_008um_00388_00419-1 44 0 4 1 1
## s_008um_00388_00420-1 47 0 8 1 1
## s_008um_00388_00421-1 50 0 11 1 1Now, we filter to only T cells, B cells and Myeloid bins, and investigate – within their neighborhood – the spatial proximity differences to Fibroblast or Tumor bins in terms of abundance.
source <- df$CT %in% c("T cells", "B cells", "Myeloid")
target <- c("Tumor", "Fibroblast", "CT")
fd <- pivot_longer(
df[source, target], cols=-CT,
names_to="target", values_to="n")
head(fd)## # A tibble: 6 × 3
## CT target n
## <chr> <chr> <int>
## 1 Myeloid Tumor 0
## 2 Myeloid Fibroblast 43
## 3 B cells Tumor 0
## 4 B cells Fibroblast 50
## 5 B cells Tumor 0
## 6 B cells Fibroblast 50In this region, we observe more fibroblast than tumor bins around each of the immune bins. However, note that in this zoomed region, we have nearly 1.4 times more fibroblast bins compared to tumor bins. Thus, we need to scale abundance values by the number of target cell type bins observed in the region.
n_tum <- table(.vhd8$.DeconLabel1)["Tumor"]
n_fib <- table(.vhd8$.DeconLabel1)["Fibroblast"]
fd$p <- ifelse(fd$target == "Tumor", fd$n/n_tum, fd$n/n_fib)
ggplot(fd, aes(x=CT, y=n, fill=target)) +
labs(y="# cells within 200px") +
ggplot(fd, aes(x=CT, y=p, fill=target)) +
labs(y="relative abundance") +
plot_layout(nrow=1, guides="collect") &
geom_boxplot(key_glyph="point") &
scale_fill_manual(values=c("yellow", "pink")) &
guides(fill=guide_legend(override.aes=list(shape=21, size=2))) &
theme_classic() & theme(
axis.title.x=element_blank(),
legend.key.size=unit(0, "lines"))
After correction, we see amplified differences in the relative abundance of fibroblast bins compared to tumor bins around each immune cell type in the zoomed region.
We can also investigate the “cell-cell-marker” relationship based on the minimum distance between two cell types within a given radius. The matrix of minimum distance between all pairwise cell types is stored in the reduced dimension, similar to how the abundance matrix was previously stored.
.vhd8 <- getDistances(.vhd8, cellType=".DeconLabel1", imageID="sample_id",
spatialCoords=names(xy), maxDist = 200, nCores=4)Here, we aim to determin whether the expression level of the tumor marker changes in proximity to fibroblast. Using FTH1 as an example, we observe a decrease in FTH1 expression in tumor bins as the distance to fibroblast increase. This trend could be visualized using a scatter plot.
p <- plotStateChanges(cells = .vhd8, type = "distances", imageID = "sample_id",
image = "sample01", cellType = ".DeconLabel1",
from = "Tumor", to = "Fibroblast", marker = "FTH1",
spatialCoords = c("array_col", "array_row"),
size = 1, shape = 19, interactive = FALSE,
plotModelFit = FALSE, method = "lm")
p$image + facet_null() | p$scatter + facet_null()
The same analysis steps could be applied to the (unfiltered) 16 μm resolution data with the corresponding deconvolution results.
In this workflow, we demonstrated how to perform a standard analysis pipeline on a subset of Visium HD data binned at 16 µm. Most methods and visualization techqniues developed for Visium can be applied to Visium HD. However, more sparsity is expected, as each data point (e.g. 2, 8, or 16 µm) has smaller area compared to Visium (55 µm). Each bin cannot be interpreted as a cell, but rather, a segmentation-free approach is applied here. Below are some resources and news for Visium HD to consider and incorporate into your pipeline:
GitHub repository of bin2cell for H&E segmentation and obtaining cell boundaries.
Analysis pipeline by 10x Genomics (April 2024). The analysis consists of three steps: run StarDist nuclei segmentation in Python, sum gene expression into nuclei based on segmentation mask, and downstream analysis with scanpy and anndata.
10x Genomics news on morphology-driven segmentation from H&E enabled in CytAssist for Visium HD, by assigning transcripts to individual cells.
Pipeline on cell typing for Visium HD in Python by Sanofi.