Tutorial: https://interactivereport.github.io/SpaceSequest/tutorial/docs/index.html
Preprint: https://www.biorxiv.org/content/10.1101/2025.09.15.676389v2
Cover Image: Overview of SpaceSequest components. SpaceSequest contains five modules to perform standarized data analysis generated from 10x Genomics Visium, Visium HD, and Xenium, as well as Bruker (acquired from the previous NanoString Technologies) GeoMx and CosMx platforms.
SpaceSequest can be installed through conda environment. We have tested the installation on Linux servers. Before proceeding, please ensure that Conda is installed and available on your system:
which conda
# Your conda path will be returned
Then navigate to the directory where you would like to install the pipeline ($PipelineDir in the following example), execute the installation script as below:
git clone https://github.com/interactivereport/SpaceSequest.git
cd SpaceSequest
# This step may take a while. Thank you for your patience
bash install.sh
# The .env will be created under the src directory
ls $PipelineDir/SpaceSequest/src/.env
#Add pipeline scripts to $PATH
vim ~/.bash_profile
PATH=$PATH:$PipelineDir/scRNASequest
# Close the vim text editor and source the file
source ~/.bash_profile
#To verify the installation, type one of the main scripts, such as:
visium
#Output:
=== Welcome to 'visium' from SpaceRequest! ===
Please provide either a path to a folder or a config file.
An empty config file will be created if a path to a folder is provided.
SpaceSequest supports spatial transcriptomics data from five cutting-edge platforms. In this section, we demonstrate two types of spatial data analysis (10x Visium HD and Bruker/NanoString CosMx) using publicly available datasets. For workflows involving other supported platforms, please find more details in the full tutorial, and separate sections for: Visium, Visium HD, Xenium, GeoMx, and CosMx.
Here, we use a publicly available Visium HD dataset to demonstrate the visiumhd workflow in SpaceSequest.
First, download the Binned_outputs (all bin levels) and Spatial outputs folders from the Output and supplemental files tab of the following two links, and decompress them if needed:
Sample1: Mouse brain FFPE: https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-brain-he
Sample2: Mouse brain fixed frozen: https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-mouse-brain-fixed-frozen
Create directories to store these binned outputs, and download a mouse brain reference data from Azimuth in Seurat format: allen_mop_2020.rds.
~/Data/
├── allen_mop_2020.rds
├── Sample1_FFPE/
├── spatial
└── binned_outputs/
├── Sample2_Fixed/
├── spatial
└── binned_outputs/
Then launch the main workflow script:
visiumhd ~/Data/
The script above will generate two template files: a config.yml file, and a sampleMeta.csv file. Users can fill these files with the relevant information for the two public datasets:
config.yml file:
#config file for VisiumHD. Please avoid using spaces in names or paths.
project_ID: VisiumHD_demo #required
sampleMeta: ~/Data/sampleMeta.csv #path to the sampleMeta file
output_dir: ~/Data/output #output directory
bin_resolution: 8um #defaul 8um, also 16um or 2um are available
cluster_resolution: 0.3 #resolution for the FindClusters step
reference: ~/Data/allen_mop_2020.rds #path to an Azimuth reference data, optional reference_name: subclass #column name of the cell type label you would like to transfer
integrate_data: True #True or False to merge/integrate all the data in the sampleMeta file
integrate_with_harmony: True #True or False to use Harmony for integration. Default as True
sampleMeta.csv file:
Sample,Directory
Mouse_brain_FFPE,~/Data/Sample1_FFPE
Mouse_brain_FixedFrozen,~/Data/Sample2_Fixed
Once the configuration files (config.yml and sampleMeta.csv) are completed, execute the pipeline using the following command:
visiumhd ~/Data/config.yml
First, download a public dataset from the NanoString website. In this example, we use the Human Frontal Cortex FFPE dataset.
In the 'DOWNLOAD DATA' section, we chose 'Basic Data Files' to download. After decompressing the flatFiles.zip file, we will see the following input files located in the S3 directory. Run gunzip S3/* to unzip all files inside.
~/S3/
├── S3_exprMat_file.csv
├── S3_fov_positions_file.csv
├── S3_metadata_file.csv
├── S3-polygons.csv
└── S3_tx_file.csv
Then launch the main workflow script:
cosmx ~/S3/
The command above will generate two template files: a config.yml file, and a sampleMeta.csv file. Users can fill in the information as below:
config.yml file:
#config file for CosMx. Please avoid using spaces in names or paths.
integrate_with_harmony: True #True or False to use Harmony for integration. Default as True
project_ID: CoxMx_demo #required project name
sampleMeta: ~/S3/sampleMeta.csv #path to the sampleMeta file
output_dir: ~/S3/output #output directory
cluster_resolution: 0.3 #resolution for the FindClusters step, default 0.3
reference: humancortexref #Azimuth reference name, optional reference_name: subclass #column name of the cell type label you would like to transfer. Required when reference is used.
integrate_data: True #True or False to merge/integrate all the data in the sampleMeta file
integrate_with_harmony: True #True or False to use Harmony for integration. Default as True
sampleMeta.csv file:
Sample,Directory,exprMat_file,fov_file,metadata_file,tx_file,polygon_file
S3,~/S3,S3_exprMat_file.csv,S3_fov_positions_file.csv,S3_metadata_file.csv,S3_tx_file.csv,S3-polygons.csv
Finally, execute the pipeline:
cosmx ~/S3/config.yml
Each workflow in SpaceSequest generates a unique set of output files, depending on the analytical steps associated with the pipeline.
In general, the outputs include:
Key result files produced by various analysis tools (e.g., BayesSpace, cell2location, etc.).
Figures for essential steps, supporting basic data visualization.
Outputs for further exploration - For example, the GeoMx workflow provides Quickomics-ready files to enable in-depth data exploration.
Final Seurat object or h5ad file.
For a complete list of output files and their descriptions, please refer to the full tutorial.
The pipeline is under MIT license.