---
description: Combining single-cell gene expression data with spatial
  information to reveal the spatial distribution of gene activity within
  tissues.
subtitle:  Seurat Toolkit
title:  Spatial Transcriptomics
---

<div>

> **Note**
>
> Code chunks run R commands unless otherwise specified.

</div>

This tutorial is adapted from the [Seurat
vignette](https://satijalab.org/seurat/v3.2/spatial_vignette.html).

Spatial transcriptomic data with the Visium platform is in many ways
similar to scRNAseq data. It contains UMI counts for 5-20 cells instead
of single cells, but is still quite sparse in the same way as scRNAseq
data is, but with the additional information about spatial location in
the tissue.\
Here we will first run quality control in a similar manner to scRNAseq
data, then QC filtering, dimensionality reduction, integration and
clustering. Then we will use scRNAseq data from mouse cortex to run
label transfer to predict celltypes in the Visium spots.\
We will use two **Visium** spatial transcriptomics dataset of the mouse
brain (Sagittal), which are publicly available from the [10x genomics
website](https://support.10xgenomics.com/spatial-gene-expression/datasets/).
Note, that these dataset have already been filtered for spots that does
not overlap with the tissue.

## Preparation

Load packages

``` {r packages}
# remotes::install_github('satijalab/seurat-data', dependencies=FALSE)

suppressPackageStartupMessages({
    library(Matrix)
    library(dplyr)
    library(SeuratData)
    library(Seurat)
    library(ggplot2)
    library(patchwork)
    library(dplyr)
})
```

Load ST data

The package `SeuratData` has some seurat objects for different datasets.
Among those are spatial transcriptomics data from mouse brain and
kidney. Here we will download and process sections from the mouse brain.

``` {r load}
# download pre-computed data if missing or long compute
fetch_data <- TRUE

# url for source and intermediate data
path_data <- "https://export.uppmax.uu.se/naiss2023-23-3/workshops/workshop-scrnaseq"

outdir <- "data/spatial/"
if (!dir.exists(outdir)) dir.create(outdir, showWarnings = F)

# to list available datasets in SeuratData you can run AvailableData()

# first we dowload the dataset
if (!("stxBrain.SeuratData" %in% rownames(SeuratData::InstalledData()))) {
    InstallData("stxBrain")
}

# now we can list what datasets we have downloaded
InstalledData()

# now we will load the seurat object for one section
brain1 <- LoadData("stxBrain", type = "anterior1")
brain2 <- LoadData("stxBrain", type = "posterior1")
```

Merge into one seurat object

``` {r}
brain <- merge(brain1, brain2)
brain
```

As you can see, now we do not have the assay **RNA**, but instead an
assay called **Spatial**.

## Quality control

Similar to scRNA-seq we use statistics on number of counts, number of
features and percent mitochondria for quality control.

Now the counts and feature counts are calculated on the Spatial assay,
so they are named **nCount_Spatial** and **nFeature_Spatial**.

``` {r}
#| fig-height: 8
#| fig-width: 8

brain <- PercentageFeatureSet(brain, "^mt-", col.name = "percent_mito")
brain <- PercentageFeatureSet(brain, "^Hb.*-", col.name = "percent_hb")

VlnPlot(brain, features = c("nCount_Spatial", "nFeature_Spatial", "percent_mito", "percent_hb"), pt.size = 0.1, ncol = 2) + NoLegend()
```

We can also plot the same data onto the tissue section.

``` {r}
#| fig-height: 15
#| fig-width: 5

SpatialFeaturePlot(brain, features = c("nCount_Spatial", "nFeature_Spatial", "percent_mito", "percent_hb"))
```

As you can see, the spots with low number of counts/features and high
mitochondrial content are mainly towards the edges of the tissue. It is
quite likely that these regions are damaged tissue. You may also see
regions within a tissue with low quality if you have tears or folds in
your section.\
But remember, for some tissue types, the amount of genes expressed and
proportion mitochondria may also be a biological features, so bear in
mind what tissue you are working on and what these features mean.

### Filter spots

Select all spots with less than **25%** mitocondrial reads, less than
**20%** hb-reads and **500** detected genes. You must judge for yourself
based on your knowledge of the tissue what are appropriate filtering
criteria for your dataset.

``` {r}
brain <- brain[, brain$nFeature_Spatial > 500 & brain$percent_mito < 25 & brain$percent_hb < 20]
```

And replot onto tissue section:

``` {r}
#| fig-height: 12
#| fig-width: 5

SpatialFeaturePlot(brain, features = c("nCount_Spatial", "nFeature_Spatial", "percent_mito"))
```

### Top expressed genes

As for scRNA-seq data, we will look at what the top expressed genes are.

``` {r}
#| fig-height: 6
#| fig-width: 6

C <- GetAssayData(brain, assay = "Spatial", slot = "counts")
C@x <- C@x / rep.int(colSums(C), diff(C@p))
most_expressed <- order(Matrix::rowSums(C), decreasing = T)[20:1]
boxplot(as.matrix(t(C[most_expressed, ])),
    cex = 0.1, las = 1, xlab = "% total count per cell",
    col = (scales::hue_pal())(20)[20:1], horizontal = TRUE
)
rm(C)
gc()
```

As you can see, the mitochondrial genes are among the top expressed
genes. Also the lncRNA gene Bc1 (brain cytoplasmic RNA 1). Also one
hemoglobin gene.

### Filter genes

We will remove the *Bc1* gene, hemoglobin genes (blood contamination)
and the mitochondrial genes.

``` {r}
dim(brain)

# Filter Bl1
brain <- brain[!grepl("Bc1", rownames(brain)), ]

# Filter Mitocondrial
brain <- brain[!grepl("^mt-", rownames(brain)), ]

# Filter Hemoglobin gene (optional if that is a problem on your data)
brain <- brain[!grepl("^Hb.*-", rownames(brain)), ]

dim(brain)
```

## Analysis

We will proceed with the data in a very similar manner to scRNA-seq
data.

For ST data, the Seurat team recommends to use `SCTransform()` for
normalization, so we will do that. `SCTransform()` will select variable
genes and normalize in one step.

``` {r}
#| results: hide
brain <- SCTransform(brain, assay = "Spatial", method = "poisson", verbose = TRUE)
```

Now we can plot gene expression of individual genes, the gene *Hpca* is
a strong hippocampal marker and *Ttr* is a marker of the choroid plexus.

``` {r}
#| fig-height: 9
#| fig-width: 8

SpatialFeaturePlot(brain, features = c("Hpca", "Ttr"))
```

If you want to see the tissue better you can modify point size and
transparency of the points.

``` {r}
#| fig-height: 5
#| fig-width: 8

SpatialFeaturePlot(brain, features = "Ttr", pt.size.factor = 1, alpha = c(0.1, 1))
```

### Dimensionality reduction and clustering

We can then now run dimensionality reduction and clustering using the
same workflow as we use for scRNA-seq analysis.

But make sure you run it on the `SCT` assay.

``` {r}
brain <- RunPCA(brain, assay = "SCT", verbose = FALSE)
brain <- FindNeighbors(brain, reduction = "pca", dims = 1:30)
brain <- FindClusters(brain, verbose = FALSE)
brain <- RunUMAP(brain, reduction = "pca", dims = 1:30)
```

We can then plot clusters onto umap or onto the tissue section.

``` {r}
#| fig-height: 4
#| fig-width: 9

DimPlot(brain, reduction = "umap", group.by = c("ident", "orig.ident"))
```

``` {r}
#| fig-height: 5
#| fig-width: 9

SpatialDimPlot(brain)
```

We can also plot each cluster separately

``` {r}
#| fig-height: 15
#| fig-width: 15

SpatialDimPlot(brain, cells.highlight = CellsByIdentities(brain), facet.highlight = TRUE, ncol = 5)
```

### Integration

Quite often, there are strong batch effects between different ST
sections, so it may be a good idea to integrate the data across
sections.

We will do a similar integration as in the Data Integration lab, but
this time we will use the SCT assay for integration. Therefore we need
to run `PrepSCTIntegration()` which will compute the sctransform
residuals for all genes in both the datasets.

``` {r}
#| results: hide

# create a list of the original data that we loaded to start with
st.list <- list(anterior1 = brain1, posterior1 = brain2)

# run SCT on both datasets
st.list <- lapply(st.list, SCTransform, assay = "Spatial", method = "poisson")

# need to set maxSize for PrepSCTIntegration to work
options(future.globals.maxSize = 2000 * 1024^2) # set allowed size to 2K MiB

st.features <- SelectIntegrationFeatures(st.list, nfeatures = 3000, verbose = FALSE)
st.list <- PrepSCTIntegration(object.list = st.list, anchor.features = st.features, verbose = FALSE)
```

Now we can perform the actual integration.

``` {r}
int.anchors <- FindIntegrationAnchors(object.list = st.list, normalization.method = "SCT", verbose = FALSE, anchor.features = st.features)
brain.integrated <- IntegrateData(anchorset = int.anchors, normalization.method = "SCT", verbose = FALSE)

rm(int.anchors, st.list)
gc()
```

Then we run dimensionality reduction and clustering as before.

``` {r}
brain.integrated <- RunPCA(brain.integrated, verbose = FALSE)
brain.integrated <- FindNeighbors(brain.integrated, dims = 1:30)
brain.integrated <- FindClusters(brain.integrated, verbose = FALSE)
brain.integrated <- RunUMAP(brain.integrated, dims = 1:30)
```

``` {r}
#| fig-height: 4
#| fig-width: 9

DimPlot(brain.integrated, reduction = "umap", group.by = c("ident", "orig.ident"))
```

``` {r}
#| fig-height: 5
#| fig-width: 9

SpatialDimPlot(brain.integrated)
```

<div>

> **Discuss**
>
> Do you see any differences between the integrated and non-integrated
> clustering? Judge for yourself, which of the clusterings do you think
> looks best? As a reference, you can compare to brain regions in the
> [Allen brain
> atlas](https://mouse.brain-map.org/experiment/thumbnails/100042147?image_type=atlas).

</div>

### Spatially Variable Features

There are two main workflows to identify molecular features that
correlate with spatial location within a tissue. The first is to perform
differential expression based on spatially distinct clusters, the other
is to find features that have spatial patterning without taking clusters
or spatial annotation into account. First, we will do differential
expression between clusters just as we did for the scRNAseq data before.

``` {r}
#| fig-height: 9
#| fig-width: 12

# differential expression between cluster 1 and cluster 6
de_markers <- FindMarkers(brain.integrated, ident.1 = 5, ident.2 = 6)

# plot top markers
SpatialFeaturePlot(object = brain.integrated, features = rownames(de_markers)[1:3], alpha = c(0.1, 1), ncol = 3)
```

Spatial transcriptomics allows researchers to investigate how gene
expression trends varies in space, thus identifying spatial patterns of
gene expression. For this purpose there are multiple methods, such as
SpatailDE, SPARK, Trendsceek, HMRF and Splotch.

In `FindSpatiallyVariables()` the default method in Seurat (method =
'markvariogram'), is inspired by the Trendsceek, which models spatial
transcriptomics data as a mark point process and computes a 'variogram',
which identifies genes whose expression level is dependent on their
spatial location. More specifically, this process calculates gamma(r)
values measuring the dependence between two spots a certain "r" distance
apart. By default, we use an r-value of '5' in these analyses, and only
compute these values for variable genes (where variation is calculated
independently of spatial location) to save time.

<div>

> **Caution**
>
> Takes a long time to run, so skip this step for now!

</div>

``` {r}
# brain <- FindSpatiallyVariableFeatures(brain, assay = "SCT", features = VariableFeatures(brain)[1:1000],
#     selection.method = "markvariogram")

# We would get top features from SpatiallyVariableFeatures
# top.features <- head(SpatiallyVariableFeatures(brain, selection.method = "markvariogram"), 6)
```

## Single cell data

We can use a scRNA-seq dataset as a reference to predict the proportion
of different celltypes in the Visium spots. Keep in mind that it is
important to have a reference that contains all the celltypes you expect
to find in your spots. Ideally it should be a scRNA-seq reference from
the exact same tissue. We will use a reference scRNA-seq dataset of
\~14,000 adult mouse cortical cell taxonomy from the Allen Institute,
generated with the SMART-Seq2 protocol.

First download the seurat data:

``` {r}
if (!dir.exists("data/spatial/visium")) dir.create("data/spatial/visium", recursive = TRUE)
path_file <- "data/spatial/visium/allen_cortex.rds"
if (!file.exists(path_file)) download.file(url = file.path(path_data, "spatial/visium/allen_cortex.rds"), destfile = path_file)
```

For speed, and for a more fair comparison of the celltypes, we will
subsample all celltypes to a maximum of 200 cells per class
(`subclass`).

``` {r}
ar <- readRDS("data/spatial/visium/allen_cortex.rds")

# check number of cells per subclass
table(ar$subclass)

# select 200 cells per subclass, fist set subclass ass active.ident
Idents(ar) <- ar$subclass
ar <- subset(ar, cells = WhichCells(ar, downsample = 200))

# check again number of cells per subclass
table(ar$subclass)
```

Then run normalization and dimensionality reduction.

``` {r}
#| fig-height: 5
#| fig-width: 7

# First run SCTransform and PCA
ar <- SCTransform(ar, ncells = 3000, verbose = FALSE, method = "poisson") %>%
    RunPCA(verbose = FALSE) %>%
    RunUMAP(dims = 1:30)

# the annotation is stored in the 'subclass' column of object metadata
DimPlot(ar, label = TRUE)
```

## Subset ST for cortex

Since the scRNAseq dataset was generated from the mouse cortex, we will
subset the visium dataset in order to select mainly the spots part of
the cortex. Note that the integration can also be performed on the whole
brain slice, but it would give rise to false positive cell type
assignments and therefore it should be interpreted with more care.

``` {r}
#| fig-height: 4
#| fig-width: 9

# subset for the anterior dataset
cortex <- subset(brain.integrated, subset = orig.ident == "anterior1")

# there seems to be an error in the subsetting, so the posterior1 image is not removed, do it manually
cortex@images$posterior1 <- NULL

# add coordinates to metadata
# note that this only returns one slide by default
cortex$imagerow <- GetTissueCoordinates(cortex)$imagerow
cortex$imagecol <- GetTissueCoordinates(cortex)$imagecol

# subset for a specific region
cortex <- subset(cortex, subset = imagerow > 400 | imagecol < 150, invert = TRUE)
cortex <- subset(cortex, subset = imagerow > 275 & imagecol > 370, invert = TRUE)
cortex <- subset(cortex, subset = imagerow > 250 & imagecol > 440, invert = TRUE)

# also subset for Frontal cortex clusters
cortex <- subset(cortex, subset = seurat_clusters %in% c(1, 2, 3, 4, 5))

p1 <- SpatialDimPlot(cortex, crop = TRUE)
p2 <- SpatialDimPlot(cortex, crop = FALSE, pt.size.factor = 1, label.size = 3)
p1 + p2
```

## Deconvolution

Deconvolution is a method to estimate the abundance (or proportion) of
different celltypes in a bulkRNAseq dataset using a single cell
reference. As the Visium data can be seen as a small bulk, we can both
use methods for traditional bulkRNAseq as well as methods especially
developed for Visium data. Some methods for deconvolution are DWLS,
cell2location, Tangram, Stereoscope, RCTD, SCDC and many more.

Here we will use SCDC for deconvolution of celltypes in the Visium
spots. For more information on the tool please check their website:
https://meichendong.github.io/SCDC/articles/SCDC.html. First, make sure
the packages you need are installed.

``` {r}
inst <- installed.packages()

if (!("xbioc" %in% rownames(inst))) {
    remotes::install_github("renozao/xbioc", dependencies = FALSE)
}
if (!("SCDC" %in% rownames(inst))) {
    remotes::install_github("meichendong/SCDC", dependencies = FALSE)
}

suppressPackageStartupMessages(library(SCDC))
suppressPackageStartupMessages(library(Biobase))
```

### Select genes for deconvolution

Most deconvolution methods does a prior gene selection and there are
different options that are used: - Use variable genes in the SC data. -
Use variable genes in both SC and ST data - DE genes between clusters in
the SC data.\
In this case we will use top DE genes per cluster, so first we have to
run DGE detection on the scRNAseq data.

For SCDC we want to find unique markers per cluster, so we select top 20
DEGs per cluster. Ideally you should run with a larger set of genes,
perhaps 100 genes per cluster to get better results. However, for the
sake of speed, we are now selecting only top20 genes and it still takes
about 10 minutes to run.

``` {r}
ar@active.assay <- "RNA"

markers_sc <- FindAllMarkers(ar,
    only.pos = TRUE,
    logfc.threshold = 0.1,
    test.use = "wilcox",
    min.pct = 0.05,
    min.diff.pct = 0.1,
    max.cells.per.ident = 200,
    return.thresh = 0.05,
    assay = "RNA"
)

# Filter for genes that are also present in the ST data
markers_sc <- markers_sc[markers_sc$gene %in% rownames(cortex), ]


# Select top 20 genes per cluster, select top by first p-value, then absolute diff in pct, then quota of pct.
markers_sc$pct.diff <- markers_sc$pct.1 - markers_sc$pct.2
markers_sc$log.pct.diff <- log2((markers_sc$pct.1 * 99 + 1) / (markers_sc$pct.2 * 99 + 1))
markers_sc %>%
    group_by(cluster) %>%
    top_n(-100, p_val) %>%
    top_n(50, pct.diff) %>%
    top_n(20, log.pct.diff) -> top20
m_feats <- unique(as.character(top20$gene))
```

### Create Expression Sets

For SCDC both the SC and the ST data need to be in the format of an
Expression set with the count matrices as `AssayData`. We also subset
the matrices for the genes we selected in the previous step.

``` {r}
eset_SC <- ExpressionSet(
    assayData = as.matrix(ar@assays$RNA@counts[m_feats, ]),
    phenoData = AnnotatedDataFrame(ar@meta.data)
)
eset_ST <- ExpressionSet(assayData = as.matrix(cortex@assays$Spatial@counts[m_feats, ]), phenoData = AnnotatedDataFrame(cortex@meta.data))
```

### Deconvolve

We then run the deconvolution defining the celltype of interest as
"subclass" column in the single cell data.

<div>

> **Caution**
>
> This is a slow compute intensive step, we will not run this now and
> instead use a pre-computed file in the step below.

</div>

``` {r}
#| results: hide
#| eval: false
# this code block is not executed

# fetch_data is defined at the top of this document
if (!fetch_data) {
  deconvolution_crc <- SCDC::SCDC_prop(
    bulk.eset = eset_ST,
    sc.eset = eset_SC,
    ct.varname = "subclass",
    ct.sub = as.character(unique(eset_SC$subclass))
  )
  saveRDS(deconvolution_crc, "data/spatial/visium/seurat_scdc.rds")
}
```

Download the precomputed file.

``` {r}
# fetch_data is defined at the top of this document
path_file <- "data/spatial/visium/seurat_scdc.rds"
if (fetch_data) {
  if (!file.exists(path_file)) download.file(url = file.path(path_data, "spatial/visium/results/seurat_scdc.rds"), destfile = path_file)
}
```

``` {r}
deconvolution_crc <- readRDS(path_file)
```

Now we have a matrix with predicted proportions of each celltypes for
each visium spot in `prop.est.mvw`.

``` {r}
head(deconvolution_crc$prop.est.mvw)
```

Now we take the deconvolution output and add it to the Seurat object as
a new assay.

``` {r}
cortex@assays[["SCDC"]] <- CreateAssayObject(data = t(deconvolution_crc$prop.est.mvw))

# Seems to be a bug in SeuratData package that the key is not set and any plotting function etc. will throw an error.
if (length(cortex@assays$SCDC@key) == 0) {
    cortex@assays$SCDC@key <- "scdc_"
}
```

``` {r}
#| fig-height: 5
#| fig-width: 8

DefaultAssay(cortex) <- "SCDC"
SpatialFeaturePlot(cortex, features = c("L2/3 IT", "L4"), pt.size.factor = 1.6, ncol = 2, crop = TRUE)
```

Based on these prediction scores, we can also predict cell types whose
location is spatially restricted. We use the same methods based on
marked point processes to define spatially variable features, but use
the cell type prediction scores as the "marks" rather than gene
expression.

``` {r}
#| fig-height: 9
#| fig-width: 8
#| eval: false
# FindSpatiallyVariableFeatures() does not work with markvariogram or moransi
# this chunk is disabled

cortex <- FindSpatiallyVariableFeatures(cortex,
    assay = "SCDC", selection.method = "markvariogram",
    features = rownames(cortex), r.metric = 5, slot = "data"
)
top.clusters <- head(SpatiallyVariableFeatures(cortex), 4)
SpatialPlot(object = cortex, features = top.clusters, ncol = 2)
```

We can also visualize the scores per cluster in ST data.

``` {r}
#| fig-height: 7.5
#| fig-width: 7
#| eval: false
# this chunk is disabled

VlnPlot(cortex, group.by = "seurat_clusters", features = top.clusters, pt.size = 0, ncol = 2)
```

Keep in mind that the deconvolution results are just predictions,
depending on how well your scRNAseq data covers the celltypes that are
present in the ST data and on how parameters, gene selection etc. are
tuned you may get different results.

<div>

> **Discuss**
>
> Subset for another region that does not contain cortex cells and check
> what you get from the label transfer. Suggested region is the right
> end of the posterial section that you can select like this:
>
> ``` {r}
> #| fig-height: 6
> #| fig-width: 9
>
> # subset for the anterior dataset
> subregion <- subset(brain.integrated, subset = orig.ident == "posterior1")
>
> # there seems to be an error in the subsetting, so the posterior1 image is not removed, do it manually
> subregion@images$anterior1 <- NULL
>
> # add coordinates to metadata
> # note that this only returns one slide by default
> subregion$imagerow <- GetTissueCoordinates(subregion)$imagerow
> subregion$imagecol <- GetTissueCoordinates(subregion)$imagecol
>
> # subset for a specific region
> subregion <- subset(subregion, subset = imagecol > 400, invert = FALSE)
>
> p1 <- SpatialDimPlot(subregion, crop = TRUE, label = TRUE)
> p2 <- SpatialDimPlot(subregion, crop = FALSE, label = TRUE, pt.size.factor = 1, label.size = 3)
> p1 + p2
> ```

</div>

## Session info

```{=html}
<details>
```
```{=html}
<summary>
```
Click here
```{=html}
</summary>
```
``` {r}
sessionInfo()
```

```{=html}
</details>
```