Select your data platform

This chapter is divided into six sub-tutorials according to the run_type platform. Each tutorial covers the corresponding starting steps for analysis. Before running the workflow, you need to organize the raw data according to the platform requirements and prepare the sample list file sample.txt so that the SpatialData zarr object can be created correctly.

• Complete input file checklist, including required and optional files, file formats, and filename patterns

• File sources and how to obtain them, such as official platform downloads, experimental outputs, or placeholder paths

• Reproducible directory structure examples

• Example integrate commands and matching sample.txt formats

Every spatial transcriptomics platform provides its own post-sequencing analysis software, such as Space Ranger for 10x Genomics data or SAW for BGI (formerly MGI) platforms. These tools perform basic data processing tasks, including alignment of raw FASTQ reads and transcript counting. Spatialsnake takes the standardized outputs from each platform and integrates them into a unified SpatialData object, streamlining all downstream analyses and visualizations. For every supported platform, Spatialsnake offers a dedicated tutorial to help you get started quickly. Please first follow the instructions below to set up the basic directory structure:

mkdir -p project_root/data project_root/results
touch project_root/sample.txt

project_root/ (current working directory)
├── data/ (stores your raw data)
├── sample.txt (key sample description file)
└── results/ (stores analysis outputs; generated automatically)

After identifying your data platform, download and store the platform-specific output files under the data/ directory, using the sample name as the subdirectory name. Ensure that the folder hierarchy follows the official output structure of that platform. At the same time, add the corresponding sample name to sample.txt so that Spatialsnake can correctly read the input files. Whether your goal is single-sample analysis or you have multiple samples from different experimental conditions and intend to perform multi-sample integration, we recommend first selecting the tutorial for your specific platform to learn the basic workflow. The multi-sample analysis pipeline is broadly similar once you understand the fundamentals.

Quick reference for run_type

run_type	Output type	Tutorial page
visium	.zarr	Start with 10x Genomics Visium
visium_segment	.zarr	Visium Segment Input Tutorial
visium_HD	.zarr	Visium HD Input Tutorial
xenium	.zarr	Xenium Input Tutorial
Merfish	.zarr	MERFISH Input Tutorial
stereo_seq	.zarr	Stereo-seq Input Tutorial

For each supported platform, we provide a public demonstration dataset.

run_type	Demo dataset	Source	Download link
visium	Visium_BreastCancer_Section1	10x Genomics	E-MTAB-11114
visium_HD	VisiumHD_MouseBrain_Demo	10x Genomics	Human CRC P2
visium_segment	Visium_Segmentation_Demo	10x Genomics / Lab output	multisample_raw_data.tar.gz
xenium	Xenium_Human_Breast_Demo	10x Genomics	Xenium Prime FFPE
Merfish	MERFISH_Vizgen_Demo	Vizgen	Vizgen MERFISH
Stereo-seq	Stereo-seq Mouse_Brain demo	Public repository	STOmics Mouse_Brain

Note

If you want to gain a basic understanding of SpatialSnake’s functionality using our sample data, please jump directly to Core Analysis Workflow and follow the instructions to proceed.

Detailed tutorials by data type

• Start with 10x Genomics Visium
• Visium HD Input Tutorial
• Visium Segment Input Tutorial
• Xenium Input Tutorial
• MERFISH Input Tutorial
• Stereo-seq Input Tutorial

Note

If you want to run multi-sample integration analysis, we recommend moving to Spatialsnake for multi-sample integration after first reading the single-sample tutorials for the basic workflow.