Introduction to Visium Analysis

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Last updated: 2025-05-12

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Knit directory: DigitalResearchSkillsNetwork/

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Please try to follow the instructions below prior to attending the workshop.

Be aware!
It is difficult to debug over zoom.
If you want the best feedback and experience, make sure you attend the workshop in person!

You can download the script on the top right or from this link.

Introduction

Setup

• Create folders

• Install Packages

• Download Data

Create Folders

├── YourFolder
│     └── raw (where we can deposit raw downloaded data)
│     └── data (where we can process data and store it)
│     └── plots (where we can store plots)

if(!dir.exists("raw")){dir.create("raw")}
if(!dir.exists("data")){dir.create("data")}
if(!dir.exists("plots")){dir.create("plots")}

Install Packages

install.packages("Seurat")
install.packages("tidyverse")
install.packages("cowplot")

BiocManager::install("GEOquery")

We have a function that will let you check the setup

source("https://github.com/DrThomasOneil/Digital-Research-Skills-Network/raw/refs/heads/main/docs/adit/checksetup.R")

checkSetup(
  cran_packages = c("Seurat", "tidyverse", "cowplot"), 
  bioc_packages=c("GEOquery") 
)

--------------------------------------

***Installing General Packages***

Seurat is loaded!
tidyverse is loaded!
cowplot is loaded!
GEOquery is loaded!

--------------------------------------
 
 All packages are loaded!

 Happy Coding! :)

set.seed(1337)

Download data

We will download the data directly from the GEO. We’ll use this dataset, which was published in 2024. Additionally, this group have published all of their methods and scripts for analysis.

# Go to the GEO page and right click on the http link under download. It should look like this

data_file <- "https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE290350&format=file"

# download the data
if(!file.exists("raw/GSE290350_RAW.tar")){download.file(url=data_file, destfile = "raw/GSE290350_RAW.tar", method='curl')}

# repeat for metadata
if(!file.exists("raw/GSE290350_metadata.csv.gz")){download.file(url='https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE290350&format=file&file=GSE290350%5Fmetadata%2Ecsv%2Egz', destfile = "raw/GSE290350_metadata.csv.gz", method='curl')}

# and the supposed processed Seurat data
if(!file.exists("raw/GSE290350_seuratObj_spatial_dist.RDS")){download.file(url="https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE290350&format=file&file=GSE290350%5FseuratObj%5Fspatial%5Fdist%2ERDS", destfile = "raw/GSE290350_seuratObj_spatial_dist.RDS", method='curl')}

Unzip and untar the raw data

untar("raw/GSE290350_RAW.tar", exdir = "../raw/GSE290350_RAW")

samples <- list.files("raw/GSE290350_RAW", pattern = "\\.tar\\.gz$", full.names = TRUE)

for (i in seq_along(samples)) {
  tar_path <- sub("\\.gz$", "", samples[i])

  # Unzip the .tar.gz file
  GEOquery::gunzip(samples[i], destname = tar_path, overwrite = TRUE)

  output_dir <- file.path("raw/GSE290350_RAW", sub("\\.tar$", "", basename(tar_path)))
  dir.create(output_dir, showWarnings = FALSE)

  # Extract tar to output directory
  untar(tar_path, exdir = output_dir)
}

Pre-processing

Here we’ll load in the data. Fortunately, this data is quite neatly organised. When present, we can load the .h5 object. Please see the Archive page of the website to find vignettes on downloading and installing GEO-sourced objects that are not as well-organised!

We will:

• Load the data

• Add the meta data

• Check the QC, filter and process the data

Load in the data

We’ll load in the supplied metadata, and then we can load 10X data quite easily using the Load10X_Spatial function, which requires a certain format. We can load in the filtered matrix, which is filtered based on whether the initial processing detected that that spot aligned with tissue. Alternatively, you can load the raw object, and filter as you wish.

meta <- read.csv("raw/GSE290350_metadata.csv.gz")

data <- Load10X_Spatial(
  data.dir="raw/GSE290350_RAW/GSM8811056_P004/P004",
  filename = "filtered_feature_bc_matrix.h5",
  filter.matrix = F
)

Add meta data

The metadata provided is not found in the object itself. So we will run a small piece of code that lets you add metadata to a Seurat object.

data$ID <- "P004"
data <- AddMetaData(data,
                    data@meta.data %>% 
                      right_join(meta[meta$ID=="P004", ], by="ID"))

QC

There are several ways to QC transcriptomic data. First, you should check and scrutinise the original manuscript.

We will assess:

• number of genes per spot

• number of unique genes per spot

• mitochondrial, haemoglobin, and ribosomal percentage.

We’ll also filter the genes themselves, to remove noisy/pointless genes.

• genes that are not present in more than x number of spots

• high expressing spots.

Genes per spot & Unique genes per spot

This is a typical single-cell RNA analysis metric. It should be carefully considered how this data is interpretted and filtered, as this is not single cell. Spots can have double the average or half the average based on the density of cells. Indeed, this is a consideration for normalisation, but this consideration is for another workshop.

As there is no gold standard, we will demonstrate how you visualise the QC metrics and then filter.

p1=FeatureScatter(data, "nCount_Spatial", "nFeature_Spatial")+NoLegend()
# number of genes
p2=SpatialFeaturePlot(data, "nCount_Spatial", 
                   crop=F,
                   pt.size=2,
                   alpha=c(.2,1))+
  NoLegend()
# number of unique features
p3=SpatialFeaturePlot(data, "nFeature_Spatial", 
                   crop=F,
                   pt.size=2,
                   alpha=c(.2,1))+
  NoLegend()
plot_grid(p1,plot_grid(p2,p3, nrow=2))

The minimum number of Features per spot (rmin(data$nFeature_Spatial)`) and minimum number of Counts per spot (151) are greater than the paper’s cut off (presuming they did not use the filtered matrix). However, for the purposes of demonstrating how this is done:

data <- subset(data, 
               subset = nCount_Spatial > 100 & nFeature_Spatial >100)

Module Scoring

In this paper, they remove cells that are >15% Mt genes & > 10% Hb reads.

data <- PercentageFeatureSet(data, "^MT-", col.name = "percent_mito")
data <- PercentageFeatureSet(data, "^HB[^(P)]", col.name = "percent_hb")
data <- PercentageFeatureSet(data, "^RP[SL]", col.name = "percent_ribo")

feature <-  c("nCount_Spatial", "nFeature_Spatial","percent_mito","percent_hb", "percent_ribo")
sapply(data@meta.data[feature], summary) %>% 
  as_tibble(rownames = "stat") %>% 
  knitr::kable(digits = 1)

stat	nCount_Spatial	nFeature_Spatial	percent_mito	percent_hb	percent_ribo
Min.	151.0	107.0	1.9	0.0	2.3
1st Qu.	1068.2	680.0	5.4	0.0	8.0
Median	2644.5	1363.0	6.5	0.0	9.6
Mean	6114.8	1934.1	6.7	0.1	9.9
3rd Qu.	6933.2	2584.2	7.8	0.1	11.8
Max.	70736.0	9935.0	14.6	12.1	20.9

p1=SpatialFeaturePlot(data, "percent_mito", 
                   crop=F,
                   pt.size=2,
                   alpha=c(.2,1))+
  NoLegend()+ggtitle("Mito")
p2=SpatialFeaturePlot(data, "percent_hb", 
                   crop=F,
                   pt.size=2,
                   alpha=c(.2,1))+
  NoLegend()+ggtitle("Hb-genes")
p3=SpatialFeaturePlot(data, "percent_ribo", 
                   crop=F,
                   pt.size=2,
                   alpha=c(.2,1))+
  NoLegend()+ggtitle("Ribo")
plot_grid(p1,p2,p3)

Filter:

data <- subset(data, subset = percent_mito<15 & percent_hb <10)
sapply(data@meta.data[feature], summary) %>% 
  as_tibble(rownames = "stat") %>% 
  knitr::kable(digits = 1)

stat	nCount_Spatial	nFeature_Spatial	percent_mito	percent_hb	percent_ribo
Min.	151.0	107.0	1.9	0.0	2.3
1st Qu.	1067.5	681.0	5.4	0.0	8.0
Median	2654.0	1363.0	6.5	0.0	9.6
Mean	6118.8	1935.1	6.7	0.1	9.9
3rd Qu.	6952.5	2585.5	7.8	0.1	11.8
Max.	70736.0	9935.0	14.6	4.7	20.9

Filter genes

We’ll first remove certain genes. MALAT1 is highly expressed in long non-coding RNA and is pretty ubiquitous. So we dont want to filter and subsequently cluster according to the expression of this gene. Similarly, Hb genes are highly expressed in rbc, which may represent contamination. We can remove these too.

keep_genes <- rownames(data)[!rownames(data) %in% c(grep("MALAT1", rownames(data), value=T), grep("^HB[^(P)]", rownames(data), value=T))]
data <- subset(data, features = keep_genes)

Low genes can create noise and take up unnecessary space in the object. Lets remove genes according to the original manuscript, whereby a gene not found in > 2 spots is removed. You can adjust this as you wish.

table(rowSums(data@assays$Spatial@layers$counts>=1)>=2)

FALSE  TRUE 
17971 18617 
keep_genes <- rowSums(data@assays$Spatial@layers$counts >= 1) >= 2
#before filtering
dim(data)
[1] 36588  1255
data <- subset(data, features = rownames(data)[keep_genes])
# after filtering
dim(data)
[1] 18617  1255

---

We recommend taking some time to carefully consider your QC strategy. There is no universal gold standard. One common approach is to start with lenient QC, proceed with processing, and during clustering and annotation, remain aware of the relaxed filtering. If unexpected results arise, you can revisit and refine your QC. Personally, I prefer to be permissive at first, while tagging potential outliers for future consideration. For example, we initially filtered out genes not present in at least 2 spots, but I may also label genes present in fewer than 5 spots as “toQC” for later review—particularly when assessing differential expression results. Similarly, we applied a 15% mitochondrial threshold but did not filter based on ribosomal content. At a later stage, we could tighten the mitochondrial threshold to 10% and include a filter for high ribosomal spots. If unusual clusters appear, we can then assess how many of those cells would have been excluded under stricter QC.

Data Processing

Seurat is an efficient package with simple functions for standard processing.

• NormalizeData: The default is to log-normalize (Feature counts for each cell are divided by the total counts for that cell and multiplied by the scale.factor. This is then natural-log transformed using log1p). Spatial data may be better normalised with Area/cell count considered. As we don’t have cell counts per spot here, we’ll stick to the standard approach and that taken by the authors

• FindVariableFeatures: The standard approach is 2000 top variable genes + ‘vst’ selection method. (First, fits a line to the relationship of log(variance) and log(mean) using local polynomial regression (loess). Then standardizes the feature values using the observed mean and expected variance (given by the fitted line). Feature variance is then calculated on the standardized values after clipping to a maximum). Standard is 2000 genes

• ScaleData: here, we scale just the variable genes to save space. If you find yourself unable to visualise your gene of interest in subsequent visualisations, it was not in this top variable list. In that case, you can scale all data.

• RunPCA & RunUMAP: Visualisation tools. There are a few things to consider in the UMAP function, but most important is the dims argument. We’ll cover this below.

Normalisation

Again, normalisation is quite simple.

data <- NormalizeData(data, 
                      normalization.method = "LogNormalize",
                      verbose=F)

Variable Features

We’ll find the top variable features.

data <- FindVariableFeatures(data, 
                             method = 'vst', 
                             nfeatures = 2000, 
                             verbose=F)

We can output/visualise them in several ways.

VariableFeatures(data) %>% head(50)
 [1] "PI3"        "SPRR1A"     "IGLC3"      "FLG"        "IGHA2"     
 [6] "IGLC2"      "IGHA1"      "JCHAIN"     "IGHG1"      "AC019349.1"
[11] "IGKC"       "IGLC1"      "IGHG4"      "KRT10"      "IGHM"      
[16] "MUSTN1"     "CRISP3"     "CEACAM5"    "SPINK7"     "SERPINB4"  
[21] "KRTDAP"     "SERPINB3"   "PI15"       "RPTN"       "SPRR2B"    
[26] "KLK6"       "MGST1"      "KRT6B"      "SLURP1"     "ERO1A"     
[31] "CCL14"      "KRT1"       "SPRR2E"     "RNASE7"     "TOP2A"     
[36] "CRCT1"      "MT1X"       "RRM2"       "S100A7"     "DUOXA2"    
[41] "STMN1"      "LCE3D"      "PCNA"       "FAM25A"     "IGHG3"     
[46] "C15orf48"   "OLFM4"      "PTTG1"      "YOD1"       "CEACAM7"   

LabelPoints(plot = VariableFeaturePlot(data), 
            points = head(VariableFeatures(data), 50), repel = TRUE)

Scale Data

Scaling the data is also easy!

data <- ScaleData(data, 
                  features = VariableFeatures(data), #if you want to scale all features, change this to rownames(data)
        verbose=F)

PCA & UMAP

Also very easy with Seurat. We’ll determine the number of PCs to use for the UMAP using ElbowPlot

data <- RunPCA(data,
               npcs = 50,
               verbose=F)

ElbowPlot(data)

The elbow plot drops off drastically and plateaus at ~6, meaning the variation just becomes noisy around PC6-onwards. So we’ll just use the first 6.

data <- RunUMAP(data, 
                dims=1:6, 
                verbose=F)

---

Visualisation

There are several visualisations we can do:

Graph:

• UMAP visualisation

• Cluster based expressions

Spatial:

• Genes overlaid on the tissue

• Module scores overlaid on the tissue

UMAP

UMAP Visualisation

We’ll first cluster the data.

data <- data %>% 
  FindNeighbors(dims = 1:6, verbose=F) %>%
  FindClusters(resolution=1, verbose=F)

And now we can create a UMAP.

p1=UMAPPlot(data,
            label=T, label.box=T)+
  NoLegend()+
  NoAxes()+
  ggtitle("11 Clusters")

Open to view more Plotting options

Here are some additional arguments that change the default visualisations. Chop and change to see how they work:

UMAPPlot(data, 
         pt.size=2, 
         label=T, 
         label.size=10,
         label.box=T,
         label.color="white",
         repel=T)+
  NoLegend()+
  NoAxes()+
  scale_color_manual(values= c("blue4", 'blue2', 'lightblue', 'green2', 'green4','brown', 'orange', 'red', 'red3', 'red4', 'black'))+
  scale_fill_manual(values= c("blue4", 'blue2', 'lightblue', 'green2', 'green4','brown', 'orange', 'red', 'red3', 'red4', 'black'))

Featureplots

We can view the gene expression on the UMAP itself.

p2=FeaturePlot(data, 
            "CD3E",
            order=T,
            pt.size=2, 
            cols = c("yellow2", 'blue3'))+NoAxes()+NoLegend()
p3=FeaturePlot(data, 
            "F13A1",
            order=T,
            pt.size=2, 
            cols = c("yellow2", 'blue3'))+NoAxes()+NoLegend()
p4=FeaturePlot(data, 
            "COL1A1",
            order=T,
            pt.size=2, 
            cols = c("yellow2", 'blue3'))+NoAxes()+NoLegend()
p5=FeaturePlot(data, 
            "KRT5",
            order=T,
            pt.size=2,
            min.cutoff = 3,
            cols = c("yellow2", 'blue3'))+NoAxes()+NoLegend()
plot_grid(p1, plot_grid(p2,p3,p4,p5, ncol=2))

DE and DotPlots

And we can simply assess the clusters themselves using differential analysis and expression graphs.

markers <- FindAllMarkers(data, 
                          logfc.threshold = 0.1,#default - increasing speeds it up, but may miss weaker signals.
                          min.pct = 0.01, #default - can be increased to ensure that the minimum expression of genes are considered. 
                          min.diff.pct = -Inf, # you can adjust this too. IF you want there to be at least a 10% difference in percentage of spots expressing the gene, you'd change this to 0.1. Can ensure that its not only the level of expression, but that you're capturing highly expressed genes
                          verbose=F #change this to true if you want to track the speed at which DE is being calculated. It is off here to save it outputting into the document 
                          ) 
top10 <- markers %>% 
  group_by(cluster) %>% 
  top_n(wt=avg_log2FC, n=10)

Heatmap

DoHeatmap(AggregateExpression(ScaleData(data, rownames(data), verbose=F), return.seurat = T, verbose=F),
          features = top10$gene, 
          draw.lines = F)

---

DotPlot

DotPlot(data, 
        features = c("CD3E", "CLEC10A", "CD207", "KRT5", "COL1A1"), 
        cols=c("grey", "red"))

Violin

VlnPlot(data, 
        features = c("CD3E", "CLEC10A", "CD207", "KRT5", "COL1A1"))

VlnPlot(data, 
        features = c("CD3E", "CLEC10A", "CD207", "KRT5", "COL1A1"), stack=T)

Spatial

We can do a few things here.

Clusters overlaid spatially

SpatialDimPlot(data, pt.size=3)

ImageDimPlot(data, size=3)

Expressions overlaid spatially

SpatialFeaturePlot(data, 
                   features="CD3E",
                   pt.size=3)

SpatialFeaturePlot(data, 
                   features="CD3E",
                   pt.size=3)+
  scale_fill_viridis_b()

ImageFeaturePlot(data, 
                   features="CD3E",
                   size=3)

Session information

sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Australia/Sydney
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] GEOquery_2.72.0     Biobase_2.64.0      BiocGenerics_0.50.0
 [4] cowplot_1.1.3       lubridate_1.9.4     forcats_1.0.0      
 [7] stringr_1.5.1       dplyr_1.1.4         purrr_1.0.4        
[10] readr_2.1.5         tidyr_1.3.1         tibble_3.2.1       
[13] ggplot2_3.5.1       tidyverse_2.0.0     Seurat_5.2.1       
[16] SeuratObject_5.0.2  sp_2.2-0            cli_3.6.4          
[19] workflowr_1.7.1    

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3     rstudioapi_0.17.1      jsonlite_1.9.1        
  [4] magrittr_2.0.3         ggbeeswarm_0.7.2       spatstat.utils_3.1-3  
  [7] farver_2.1.2           rmarkdown_2.29         fs_1.6.5              
 [10] vctrs_0.6.5            ROCR_1.0-11            spatstat.explore_3.3-4
 [13] htmltools_0.5.8.1      sass_0.4.9             sctransform_0.4.1     
 [16] parallelly_1.42.0      KernSmooth_2.23-26     bslib_0.9.0           
 [19] htmlwidgets_1.6.4      ica_1.0-3              plyr_1.8.9            
 [22] plotly_4.10.4          zoo_1.8-13             cachem_1.1.0          
 [25] whisker_0.4.1          igraph_2.1.4           mime_0.13             
 [28] lifecycle_1.0.4        pkgconfig_2.0.3        Matrix_1.7-3          
 [31] R6_2.6.1               fastmap_1.2.0          fitdistrplus_1.2-2    
 [34] future_1.34.0          shiny_1.10.0           digest_0.6.37         
 [37] colorspace_2.1-1       patchwork_1.3.0        ps_1.9.0              
 [40] rprojroot_2.0.4        tensor_1.5             RSpectra_0.16-2       
 [43] irlba_2.3.5.1          labeling_0.4.3         progressr_0.15.1      
 [46] timechange_0.3.0       spatstat.sparse_3.1-0  httr_1.4.7            
 [49] polyclip_1.10-7        abind_1.4-8            compiler_4.4.0        
 [52] bit64_4.6.0-1          withr_3.0.2            fastDummies_1.7.5     
 [55] MASS_7.3-65            tools_4.4.0            vipor_0.4.7           
 [58] lmtest_0.9-40          beeswarm_0.4.0         httpuv_1.6.15         
 [61] future.apply_1.11.3    goftest_1.2-3          glue_1.8.0            
 [64] callr_3.7.6            nlme_3.1-167           promises_1.3.2        
 [67] grid_4.4.0             Rtsne_0.17             getPass_0.2-4         
 [70] cluster_2.1.8.1        reshape2_1.4.4         generics_0.1.3        
 [73] hdf5r_1.3.12           gtable_0.3.6           spatstat.data_3.1-6   
 [76] tzdb_0.5.0             hms_1.1.3              data.table_1.17.0     
 [79] xml2_1.3.8             spatstat.geom_3.3-6    RcppAnnoy_0.0.22      
 [82] ggrepel_0.9.6          RANN_2.6.2             pillar_1.10.1         
 [85] limma_3.60.6           spam_2.11-1            RcppHNSW_0.6.0        
 [88] later_1.4.1            splines_4.4.0          lattice_0.22-6        
 [91] bit_4.6.0              survival_3.8-3         deldir_2.0-4          
 [94] tidyselect_1.2.1       miniUI_0.1.1.1         pbapply_1.7-2         
 [97] knitr_1.50             git2r_0.35.0           gridExtra_2.3         
[100] scattermore_1.2        xfun_0.51              statmod_1.5.0         
[103] matrixStats_1.5.0      stringi_1.8.4          lazyeval_0.2.2        
[106] yaml_2.3.10            evaluate_1.0.3         codetools_0.2-20      
[109] uwot_0.2.3             xtable_1.8-4           reticulate_1.41.0.1   
[112] munsell_0.5.1          processx_3.8.6         jquerylib_0.1.4       
[115] Rcpp_1.0.14            globals_0.16.3         spatstat.random_3.3-2 
[118] png_0.1-8              ggrastr_1.0.2          spatstat.univar_3.1-2 
[121] parallel_4.4.0         presto_1.0.0           dotCall64_1.2         
[124] listenv_0.9.1          viridisLite_0.4.2      scales_1.3.0          
[127] ggridges_0.5.6         rlang_1.1.6           

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