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First you need to load the library into your R session.

library(STutility)

10X Visium platform

Input files

10X Visium data output is produced with the spaceranger command line tool from raw fastq files. The output includes a number of files, and the ones that needs to be imported into R for STUtility are the following:

  1. “filtered_feature_bc_matrix.h5” or “raw_feature_bc_matrix.h5” : Gene expression matrices in .h5 format containing the raw UMI counts for each spot and each gene
  2. “tissue_positions_list.txt” : contains capture-spot barcode spatial information and pixel coordinates
  3. “tissue_hires_image.png” : H&E image in PNG format
  4. “scalefactors_json.json” : This file contains scaling factors subject to the H&E images of different resolutions. E.g. “tissue_hires_scalef”: 0.063, implies that the pixel coordinates in the position list should be scaled with 0.063 to match the size of the hires_image.png file.

To use the full range of functions within STUtility, all four files are needed for each sample. However, all data analysis steps that do not involve the H&E image can be performed with only the count file as input. To read in the 10x Visium .h5 files, the package hdf5r needs to be installed (BiocManager::install("hdf5r")).

To follow along this tutorial with a test data set, go to the 10x Dataset repo and download the following two files:

  • Feature / cell matrix HDF5 (filtered)
  • Spatial imaging data (.zip)
    • tissue_hires_image
    • tissue_positions_list
    • scalefactors_json

The .zip file contains the H&E image (in two resolution formats; “tissue_lowres_image” and “tissue_hires_image”), the “tissue_positions_list” with pixel coordinates for the orginial .tif image and the “scalefactors_json.json” that contains the scalefactors used to dervive the pixel cooridinates for the hires images. There are some alternatives to handle the scalefactors. Either, you manualy open the .json file and note the scalefactor and state these numbers in a column in the infoTable named “scaleVisium” (see below). Or, you add a column named “json” with paths to the “scalefactors_json.json” files. A third option is to manually input the values to the function InputFromTable (see ?InputFromTable).

In this vignette we show e.g. the data sets from Mouse Brain Serial Section 1 and 2 (Sagittal-Posterior)

Prepare data

The recommended method to read the files into R is via the creation of a “infoTable”, there are three columns that the package will note whether they are included or not: “samples”, “spotfiles” and “imgs”. 

                samples                spotfiles                imgs
1 Path to count file 1  Path to position list  1 Path to H&E image 1
2  Path to count file 2 Path to position list  2 Path to H&E image 2
                 json
1 Path to json file 1
2 Path to json file 2

These contains the paths to the files. Any number of extra columns can be added to the infoTable data.frame that you want to include as meta data in your Seurat object, e.g. “gender”, “age”, “slide_id” etc. These columns can be named as you like, but not “sample”, “spotfiles” or “imgs”.

We are now ready to load our samples and create a “Seurat” object using our infotTable data.frame.

Here, we demonstrate the creation of the Seurat object, while also including some filtering (see section “Quality Control” for more information on filtering):

  • Keeping the genes that are found in at least 5 capture spots and has a total count value >= 100.
  • Keeping the capture-spots that contains >= 500 total transcripts.

Note that you have to specify which platform the data comes from. The default platform is 10X Visium but if you wish to run data from the older ST platforms, there is support for “1k” and “2k” arrays. You can also mix datasets from different platforms by specifying one of; “Visium”, “1k” or “2k” in a separate column of the infoTable named “platform”. You just have to make sure that the datasets have gene symbols which follows the same nomenclature. 

se <- InputFromTable(infotable = infoTable, 
                      min.gene.count = 100, 
                      min.gene.spots = 5,
                      min.spot.count = 500,
                      platform =  "Visium")


Once you have created a Seurat object you can process and visualize your data just like in a scRNA-seq experiment and make use of the plethora of functions provided in the Seurat package. There are many vignettes to get started available at the Seurat web site.

For example, if you wish to explore the spatial distribution of various features on the array coordinates you can do this using the ST.FeaturePlot() function. Features include any column stored in the “meta.data” slot, dimensionality reduction objects or gene expression vectors.


ST.FeaturePlot(se, features = c("nFeature_RNA"), dark.theme = T, cols = c("black", "darkblue", "cyan", "yellow", "red", "darkred"))

Version Author Date
4f42429 Ludvig Larsson 2020-06-04
fb06450 jbergenstrahle 2020-01-11
03c9b7c jbergenstrahle 2019-11-19
d96da86 Ludvig Larsson 2019-10-31


Original ST platform

In general, using STUtility for the old ST platform data follows the same workflow as for the 10X Visium arrays. The only difference is when loading the data into R.

Input files

The original ST workflow produces the following three output files:

  1. Count file (Count file with raw counts (UMI filtered) for each gene and capture spot)
  2. Spot detector output (File with spatial pixel coordinate information produces via the Spot Detector webtool)
  3. H&E image

Prepare data

The recommended method to read the files into R is via the creation of a “infoTable”, which is a table with at least three columns “samples”, “spotfiles” and “imgs”.

Test data is provided:

infoTable <- read.table("~/STUtility/inst/extdata/metaData_mmBrain.csv", sep=";", header=T, stringsAsFactors = F)[c(1, 5, 6, 7), ]

Load data and convert from EnsambleIDs to gene symbols

The provided count matrices uses EnsambleIDs (with version id) for the gene symbols. Gene symbols are often a preference for easier reading, and we have therefore included an option to directly convert the gene IDs when creating the Seurat object. The data.frame object required for conversion should have one column called “gene_id” matching the original gene IDs and a column called “gene_name” with the desired symbols. you also need to make sure that these columns have unique names, otherwise the converiion will not work. We have provided such a table that you can use to convert between EnsambleIDs and MGI symbols (mus musculus gene nomenclature).


#Transformation table for geneIDs
ensids <- read.table(file = list.files(system.file("extdata", package = "STutility"), full.names = T, pattern = "mouse_genes"), header = T, sep = "\t", stringsAsFactors = F)

We are now ready to load our samples and create a “Seurat” object.

Here, we demonstrate the creation of the Seurat object, while also including some filtering:

  • Keeping the genes that are found in at least 5 capture spots and has a total count value >= 100.
  • Keeping the capture-spots that contains >= 500 total transcripts.

Note that we specify that we’re using the “2k” array platform and also, since we in this case have genes in the columns, we set transpose=TRUE. 

#TODO: add warnings if ids missmatch. Check that ids are in the data.frame ...
se <- InputFromTable(infotable = infoTable, 
                      min.gene.count = 100, 
                      min.gene.spots = 5,
                      min.spot.count = 500, 
                      annotation = ensids, 
                      platform = "2k",
                      transpose = TRUE)


Once you have created a Seurat object you can process and visualize your data just like in a scRNA-seq experiment and make use of the plethora of functions provided in the Seurat package. There are many vignettes to get started available at the Seurat web site.

Some of the functionalities provided in the Seurat package are not yet supported by STUtility, such as dataset integration and multimodal analysis. These methods should in principle work if you treat the data like a scRNA-seq experiment, but you will not be able to make use of the image related data or the spatial visualization functions.

For example, if you wish to explore the spatial distribution of various features on the array coordinates you can do this using the ST.FeaturePlot() function. 

ST.FeaturePlot(se, features = c("nFeature_RNA"), dark.theme = T, cols = c("dark blue", "cyan", "yellow", "red", "darkred"))

Version Author Date
4f42429 Ludvig Larsson 2020-06-04
fb06450 jbergenstrahle 2020-01-11