This tutorial covers the basics of using hdWGCNA to perform co-expression network analysis on spot-based spatial transcriptomics (ST) data like 10X Genomics Visium. We demonstrate hdWGCNA using a ST dataset containing an anterior and posterior saggital section from the mouse brain. This section is similar to the hdWGCNA in single-cell data tutorial, so we suggest exploring that code first.

Load required libraries

First we will load the required R libraries for this tutorial.

# single-cell analysis package

# package to install the mouse brain dataset

# plotting and data science packages

# co-expression network analysis packages:

# install this package, which allows us to compute distance between the spots

# enable parallel processing for network analysis (optional)
enableWGCNAThreads(nThreads = 8)

# using the cowplot theme for ggplot

# set random seed for reproducibility

Download and process the mouse brain dataset

Here we use SeuratData to download the mouse brain ST dataset, and we will use the standard Seurat workflow to process it.

Note on SeuratData download

In our own testing, we had difficulty running InstallData on our institution’s compute cluster, so we ran these commands locally and then copied the .rds file containing the Seurat object to the cluster for subsequent analysis.

# download the mouse brain ST dataset (stxBrain)

# load the anterior and posterior samples
brain <- LoadData("stxBrain", type = "anterior1")
brain$region <- 'anterior'
brain2 <- LoadData("stxBrain", type = "posterior1")
brain2$region <- 'posterior'

# merge into one seurat object
seurat_obj <- merge(brain, brain2)
seurat_obj$region <- factor(as.character(seurat_obj$region), levels=c('anterior', 'posterior'))

# save unprocessed object
saveRDS(seurat_obj, file='mouse_brain_ST_unprocessed.rds')

hdWGCNA requires the spatial coordinates to be stored in the slot. Here we extract the image coordinates for the two samples, merge into a dataframe, and add it into the Specifically, the must have columns named row, col, imagerow, and imagecol (shown below), otherwise the downstream steps will not work.

# make a dataframe containing the image coordinates for each sample
image_df <-, lapply(names(seurat_obj@images), function(x){

# merge the image_df with the Seurat metadata
new_meta <- merge(, image_df, by='row.names')

# fix the row ordering to match the original seurat object
rownames(new_meta) <- new_meta$Row.names
ix <- match(as.character(colnames(seurat_obj)), as.character(rownames(new_meta)))
new_meta <- new_meta[ix,]

# add the new metadata to the seurat object <- new_meta

    tissue row col imagerow imagecol
AAACAAGTATCTCCCA-1_1      1  50 102     7475     8501
AAACACCAATAACTGC-1_1      1  59  19     8553     2788
AAACAGAGCGACTCCT-1_1      1  14  94     3164     7950
AAACAGCTTTCAGAAG-1_1      1  43   9     6637     2099
AAACAGGGTCTATATT-1_1      1  47  13     7116     2375
AAACATGGTGAGAGGA-1_1      1  62   0     8913     1480

Now we run the standard Seurat processing pipeline.

# normalization, feature selection, scaling, and PCA
seurat_obj <- seurat_obj %>%
  NormalizeData() %>%
  FindVariableFeatures() %>%
  ScaleData() %>%

# Louvain clustering and umap
seurat_obj <- FindNeighbors(seurat_obj, dims = 1:30)
seurat_obj <- FindClusters(seurat_obj,verbose = TRUE)
seurat_obj <- RunUMAP(seurat_obj, dims = 1:30)

# set factor level for anterior / posterior
seurat_mouse_vis$region <- factor(as.character(seurat_mouse_vis$region), levels=c('anterior', 'posterior'))

# show the UMAP
p1 <- DimPlot(seurat_obj, label=TRUE, reduction = "umap", = "seurat_clusters") + NoLegend()
p2 <- SpatialDimPlot(seurat_obj, label = TRUE, label.size = 3)
We used the following cluster labels for this analysis.
See cluster labels
seurat_clusters annotation
0 Caudoputamen
1 White matter
2 Cortex L5
3 White matter
4 Cortex L6
5 Medulla
6 Pons
7 Cortex L2/3
8 Cerebellum molecular layer
9 Hippocampus
10 Cortex L1, Vasculature
11 Olfactory bulb outer
12 Cerebellum arbor vitae
13 Thalamus
14 Nucleus accumbens
15 Piriform area
16 Hypothalamums
17 Fiber tracts
18 Olfactory bulb inner
19 Cerebellum granular layer
20 Ventricles
21 Olfactory bulb fiber tracts
# add annotations to Seurat object
annotations <- read.csv('annotations.csv')
ix <- match(seurat_obj$seurat_clusters, annotations$seurat_clusters)
seurat_obj$annotation <- annotations$annotation[ix]

# set idents
Idents(seurat_obj) <- seurat_obj$annotation

p3 <- SpatialDimPlot(seurat_obj, label = TRUE, label.size = 3)
p3 + NoLegend()

Construct metaspots

Sequencing-based ST approaches like Visium generate sparse expression profiles per spot thus introducing the same potential pitfalls as single-cell data for co-expression network analysis. To alleviate these issues, hdWGCNA includes a data aggregation approach to produce spatial metaspots, similar to our metacell algorithm. This approach aggregates neighboring spots based on spatial coordinates rather than their transcriptomes. This procedure is performed in hdWGCNA using the MetaspotsByGroups function.

Here we set up the data for hdWGCNA and run MetaspotsByGroups. Similar to MetacellsByGroups, the parameter slices the Seurat object to construct metaspots separately for each group. Here we are just grouping by the ST slides to perform this step separately for the anterior and posterior sample, but you could specify cluster or anatomical regions as well to suit your analysis.

seurat_obj <- SetupForWGCNA(
  gene_select = "fraction",
  fraction = 0.05,
  wgcna_name = "vis"

seurat_obj <- MetaspotsByGroups(
  seurat_obj, = c("region"), = "region",
  assay = 'Spatial',
  slot = 'counts'
seurat_obj  <- NormalizeMetacells(seurat_obj)

The metaspot object is used everywhere that the metacell object would be used for downstream analysis. For example, to extract the metaspot object, you can run the GetMetacellObject function.

m_obj <- GetMetacellObject(seurat_obj)
An object of class Seurat
31053 features across 1505 samples within 1 assay
Active assay: Spatial (31053 features, 0 variable features)

Co-expression network analysis

Now we are ready to perform co-expression network analysis using an identical pipeline to the single-cell workflow. For this analysis, we are performing brain-wide network analysis using all spots from all regions, but this analysis could be adjusted to perform network analysis on specific regions.

# set up the expression matrix, set and group_name to NULL to include all spots
seurat_obj  <- SetDatExpr(
  group_name = NULL

# test different soft power thresholds
seurat_obj <- TestSoftPowers(seurat_obj)
plot_list <- PlotSoftPowers(seurat_obj)

wrap_plots(plot_list, ncol=2)
# construct co-expression network:
seurat_obj <- ConstructNetwork(

# plot the dendrogram
PlotDendrogram(seurat_obj, main='Spatial hdWGCNA dendrogram')

Next, we compute module eigengenes (MEs) and eigengene-based connectivities (kMEs) using the ModuleEigengenes and ModuleConnectivity functions respectively.

seurat_obj <- ModuleEigengenes(seurat_obj)
seurat_obj <- ModuleConnectivity(seurat_obj)

Here we reset the module names with the prefix “SM” (spatial modules).

seurat_obj <- ResetModuleNames(
  new_name = "SM"

modules <- GetModules(seurat_obj) %>% subset(module != 'grey')
gene_name module color
gene_name module     color
Rgs20                 Rgs20    SM1       red
Oprk1                 Oprk1    SM1       red

St18                   St18    SM2 turquoise
3110035E14Rik 3110035E14Rik    SM3     brown
A830018L16Rik A830018L16Rik    SM3     brown
Sulf1                 Sulf1    SM4       tan

Data visualization

Here we visualize module eigengenes using the Seurat functions DotPlot and SpatialFeaturePlot. For network visualization, please refer to the Network visualization tutorial.

# get module eigengenes and gene-module assignment tables
MEs <- GetMEs(seurat_obj)
modules <- GetModules(seurat_obj)
mods <- levels(modules$module); mods <- mods[mods != 'grey']

# add the MEs to the seurat metadata so we can plot it with Seurat functions <- cbind(, MEs)

# plot with Seurat's DotPlot function
p <- DotPlot(seurat_obj, features=mods, = 'annotation', dot.min=0.1)

# flip the x/y axes, rotate the axis labels, and change color scheme:
p <- p +
  coord_flip() +
  RotatedAxis() +
  scale_color_gradient2(high='red', mid='grey95', low='blue') +
  xlab('') + ylab('')


We can visualize the MEs directly on the spatial coordinates using SpatialFeaturePlot.

p <- SpatialFeaturePlot(
  features = mods,
  alpha = c(0.1, 1),
  ncol = 8


png("figures/MEs_featureplot.png", height=16, width=20, units='in', res=200)

Next steps

In this tutorial we went over the core functions for performing co-expression network analysis in spatial transcriptomics data. We encourage you to explore our other tutorials for downstream analysis of these hdWGCNA results.