> For the complete documentation index, see [llms.txt](https://stereotoolss-organization.gitbook.io/stomics-software-download/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://stereotoolss-organization.gitbook.io/stomics-software-download/eco-tools/advanced-analysis.md).

# Advanced Analysis

To provide a comprehensive analysis workflow, we have curated a list of ecosystem tools compatible with Stereo-seq data. These are categorized into the STOmics Co-developed Ecosystem (offering native data support and joint optimization) and General Community Tools (requiring standard data format conversion).

## STOmics Developed

*Tools in this section offer native support for Stereo-seq data formats, ensuring a seamless data flow.*

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th><select multiple><option value="AK5HVENXAdDI" label="Python Library" color="blue"></option><option value="NFb00N6vAlmQ" label="CLI" color="blue"></option><option value="bLdmMEBlsxAD" label="Pipeline" color="blue"></option><option value="5vmJNr4a8VEx" label="Linux" color="blue"></option><option value="KYN5yDepCUXQ" label="Open Source" color="blue"></option><option value="Z3biiokg1oMj" label="Python" color="blue"></option><option value="5mEad9jVYMFt" label="3D Alignment" color="blue"></option></select></th><th></th><th></th><th data-hidden data-card-cover data-type="image">Cover image</th></tr></thead><tbody><tr><td><h3>Stereo Data Analysis Solution (SDAS) </h3></td><td><span data-option="NFb00N6vAlmQ">CLI, </span><span data-option="5vmJNr4a8VEx">Linux, </span><span data-option="KYN5yDepCUXQ">Open Source</span></td><td><a href="https://github.com/STOmics/SDAS" class="button primary medium" data-icon="github">GitHub</a></td><td><ul><li><strong>Overview</strong>: SDAS is a ready-to-run Linux command-line toolkit developed for Stereo-seq data. It built with algorithms benchmarked specifically on Stereo-seq datasets.</li><li><p><strong>Key Features &#x26; Values:</strong></p><ul><li><strong>Stereo-seq Benchmarked:</strong> Features 31 algorithms rigorous tested on Stereo-seq data, backed by an Analysis Guide to ensure reliable, high-performance results.</li><li><strong>Out-of-the-Box &#x26; Dependency-Free:</strong> Zero-configuration Linux package. Operates without manual setup of complex R, Python, or CUDA environments, eliminating dependency conflicts.</li><li><strong>14 Modules &#x26; 31 Algorithms:</strong> Consolidates 31 benchmarked spatial algorithms into 14 modular pipelines (e.g., cell annotation, cell-cell communication, and spatial domain analysis).</li><li><strong>Native Ecosystem Integration:</strong> Seamlessly ingests <code>.h5ad</code> / <code>.h5mu</code> files directly output by SAW, while maintaining compatibility with standard AnnData and Seurat objects.</li></ul></li><li><strong>Workflow Value:</strong> Replaces fragmented custom scripting with a unified, Stereo-seq-optimized analysis pipeline, enabling researchers to effortlessly unlock spatial coordinates and biological insights.</li></ul><p><strong>Blog Post</strong>: <a href="https://en.stomics.tech/resources/stomics-blog/2881.html">Reclaiming the "Lost Spatial Coordinates": Break Single-Cell Limits and Unlock Spatiotemporal Insights with SDAS</a></p></td><td data-object-fit="contain"><a href="https://640092348-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FQfDAr8IqIRwkROXEczGT%2Fuploads%2FTgLiNG9HNKt0YtJ5szqY%2FFrame%204.svg?alt=media&amp;token=a462d59d-5710-46fc-b09c-be2155eaccf6">Frame 4.svg</a></td></tr><tr><td><h3>Stereo3D</h3></td><td><span data-option="Z3biiokg1oMj">Python, </span><span data-option="5mEad9jVYMFt">3D Alignment, </span><span data-option="KYN5yDepCUXQ">Open Source</span></td><td><a href="https://github.com/STOmics/stereo3d" class="button primary medium" data-icon="github">GitHub</a></td><td><ul><li><strong>Overview</strong>: A specialized framework for 3D spatial reconstruction and multi-slice registration from serial tissue sections.
</li><li><strong>Stereo-seq Optimization</strong>: Optimized for Stereo-seq high-resolution datasets, seamlessly handling large-scale coordinates and alignment across consecutive slices.
</li><li><strong>Workflow Value</strong>: Transforms 2D STOmics slice datasets into a unified 3D spatial coordinate system for volumetric expression mapping.</li></ul></td><td data-object-fit="contain"><a href="https://640092348-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FQfDAr8IqIRwkROXEczGT%2Fuploads%2FKBSsDu0G5pCrCZsAMU7N%2Fstereo3d.svg?alt=media&amp;token=40b616b6-8725-4d0b-b034-bc33b7c7be88">stereo3d.svg</a></td></tr></tbody></table>

## Community Tools

*Widely used tools in the spatial transcriptomics community.*&#x20;

> *Note: STOmics provides this list for informational purposes only and does not officially support or maintain community-developed tools. We make no guarantees regarding their performance, stability, or compatibility. For technical support, issues, or feature requests, please contact the respective tool developers or consult their official repositories.*

### General Frameworks & Toolkits

<table data-view="cards"><thead><tr><th></th><th><select multiple><option value="5vmJNr4a8VEx" label="Linux" color="blue"></option><option value="Ogvkay6aLgV0" label="Python Toolkit" color="blue"></option><option value="8NFf0uKx6bKS" label="R Toolkit" color="blue"></option><option value="8h3ulnwokyuU" label="Open Source" color="blue"></option></select></th><th></th><th></th><th data-hidden data-card-cover data-type="image">Cover image</th></tr></thead><tbody><tr><td><h3>Scanpy</h3></td><td><span data-option="Ogvkay6aLgV0">Python Toolkit, </span><span data-option="8h3ulnwokyuU">Open Source</span></td><td><a href="https://github.com/scverse/scanpy" class="button primary medium" data-icon="github">GitHub</a><a href="https://scanpy.scverse.org/en/stable/" class="button primary medium" data-icon="readme">Doc</a><a href="https://doi.org/10.1186/s13059-017-1382-0" class="button secondary medium" data-icon="up-right-from-square">Paper</a></td><td><ul><li><strong>Overview</strong>: A scalable toolkit for analyzing single-cell gene expression data built jointly with <a href="https://anndata.readthedocs.io/"><code>anndata</code></a>. </li><li><strong>How to Bridge</strong>: Use <em>SAW / StereoMap</em> generated matrix data in <code>.h5ad</code> format, which can then be directly imported into <em>Scanpy</em> for clustering, dimensionality reduction, and visualization.</li></ul></td><td data-object-fit="contain"><a href="https://640092348-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FQfDAr8IqIRwkROXEczGT%2Fuploads%2FPtcPPKKKNiKBlO0VthHn%2Fscanpy.svg?alt=media&amp;token=e08d4be0-3bf0-4ce5-9ab8-2119998fa4f3">scanpy.svg</a></td></tr><tr><td><h3>Seurat</h3></td><td><span data-option="8NFf0uKx6bKS">R Toolkit, </span><span data-option="8h3ulnwokyuU">Open Source</span></td><td><a href="https://github.com/satijalab/seurat" class="button primary medium" data-icon="github">GitHub</a><a href="https://satijalab.org/seurat/" class="button primary medium" data-icon="readme">Doc</a><a href="https://satijalab.org/seurat/authors#citation" class="button secondary medium" data-icon="quotes">Citation</a></td><td><ul><li><strong>Overview</strong>: A highly popular R toolkit for single-cell and spatial transcriptomics analysis.</li><li><strong>How to Bridge</strong>: You can convert <code>.h5ad</code> data into a <code>Seurat Object</code> to leverage its rich R ecosystem for downstream spatial feature mining.</li></ul></td><td data-object-fit="contain"><a href="https://640092348-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FQfDAr8IqIRwkROXEczGT%2Fuploads%2F7rsoqtvPPG8k7NI1DiJG%2Fseurat.svg?alt=media&amp;token=bf62b957-d494-42f7-b908-077729d71db2">seurat.svg</a></td></tr><tr><td><h3>Spateo</h3></td><td><span data-option="Ogvkay6aLgV0">Python Toolkit, </span><span data-option="8h3ulnwokyuU">Open Source</span></td><td><a href="https://github.com/aristoteleo/spateo-release" class="button primary medium" data-icon="github">GitHub</a><a href="https://spateo-release.readthedocs.io/en/latest/index.html" class="button primary medium" data-icon="readme">Doc</a><a href="https://doi.org/10.1016/j.cell.2024.10.011" class="button secondary medium" data-icon="up-right-from-square">Paper</a></td><td><ul><li><strong>Overview</strong>: An open-source Python framework designed for quantitative spatiotemporal modeling of spatial transcriptomics data, specially optimized for high-resolution and large-field technologies like Stereo-seq.</li><li><strong>How to Bridge</strong>: Export <em>SAW / StereoMap</em> generated matrix data in <code>.h5ad</code> format, which can be directly imported into <em>Spateo</em> for advanced 3D reconstruction, cell segmentation, and morphometric vector field analysis.</li></ul></td><td data-object-fit="contain"><a href="https://640092348-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FQfDAr8IqIRwkROXEczGT%2Fuploads%2FQW5zQhvmCf8dSCntBocz%2Fspateo.svg?alt=media&amp;token=a075e6bc-1518-4490-bfa2-9432aba67c90">spateo.svg</a></td></tr><tr><td><h3>Stereopy</h3></td><td><span data-option="Ogvkay6aLgV0">Python Toolkit, </span><span data-option="8h3ulnwokyuU">Open Source</span></td><td><a href="https://github.com/STOmics/stereopy" class="button primary medium" data-icon="github">GitHub</a><a href="https://stereopy.readthedocs.io/en/latest/index.html" class="button primary medium" data-icon="readme">Doc</a><a href="https://doi.org/10.1038/s41467-025-58079-9" class="button secondary medium" data-icon="up-right-from-square">Paper</a></td><td><ul><li><strong>Overview</strong>: A comprehensive and scalable Python toolkit specifically designed for downstream spatial transcriptomics analysis, featuring advanced spatial algorithms and multi-sample analysis capabilities.</li><li><strong>How to Bridge</strong>: Natively accepts <code>.h5ad</code>, <code>.gef</code>, or <code>.h5mu</code> files directly generated by <em>SAW</em> (including multi-sample aggregated datasets from <code>saw aggr</code>). It enables downstream workflows including cell clustering, tissue zone detection, cell-cell communication, and multi-sample analysis.</li></ul></td><td data-object-fit="contain"><a href="https://640092348-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FQfDAr8IqIRwkROXEczGT%2Fuploads%2FzGp2XfeBw9qWnDk7VpGb%2Fstereopy.svg?alt=media&amp;token=6a58c8be-d13e-429b-bd4f-41a4e3c35a3e">stereopy.svg</a></td></tr><tr><td><h3>Giotto Suite</h3></td><td><span data-option="8NFf0uKx6bKS">R Toolkit, </span><span data-option="8h3ulnwokyuU">Open Source</span></td><td><a href="https://github.com/giotto-suite/Giotto" class="button primary medium" data-icon="github">GitHub</a><a href="https://giottosuite.com/" class="button primary medium" data-icon="readme">Doc</a><a href="https://doi.org/10.1038/s41592-025-02817-w" class="button secondary medium" data-icon="up-right-from-square">Paper</a></td><td><ul><li><strong>Overview</strong>: A comprehensive R package for multi-modal spatial-omics analysis, offering native data support and multi-scale capabilities optimized for Stereo-seq.</li><li><strong>How to Bridge</strong>: Directly parses Stereo-seq <code>.gef</code> matrix files or accepts <code>.h5ad</code> files via interoperability layers. It enables multi-scale analysis—from bin/cell levels to subcellular Bin1 data—for downstream spatial domain and cell-interaction mining.</li></ul></td><td data-object-fit="contain"><a href="https://640092348-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FQfDAr8IqIRwkROXEczGT%2Fuploads%2FVCFswlOAfUZjHdj0oxNR%2Fgiotto.svg?alt=media&amp;token=bdb27a4a-2d23-4f31-9f03-0a37d2e46b83">giotto.svg</a></td></tr><tr><td><h3>Squidpy</h3></td><td><span data-option="Ogvkay6aLgV0">Python Toolkit, </span><span data-option="8h3ulnwokyuU">Open Source</span></td><td><a href="https://github.com/scverse/squidpy" class="button primary medium" data-icon="github">GitHub</a><a href="https://squidpy.readthedocs.io/en/stable/" class="button primary medium" data-icon="readme">Doc</a><a href="https://doi.org/10.1038/s41592-021-01358-2" class="button secondary medium" data-icon="up-right-from-square">Paper</a></td><td><ul><li><strong>Overview</strong>: A python framework built on top of <em>Scanpy</em> and <em>AnnData</em>, specifically designed for the analysis and visualization of spatial omics data, featuring advanced spatial graph analysis and image-transcriptome integration.</li><li><strong>How to Bridge</strong>: Directly takes <code>.h5ad</code> files converted from <em>SAW/StereoMap</em>. It connects cellular spatial coordinates into spatial graphs to perform neighborhood enrichment, spatial co-occurrence, and ligand-receptor interaction analysis.</li></ul></td><td data-object-fit="contain"><a href="https://640092348-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FQfDAr8IqIRwkROXEczGT%2Fuploads%2FFrVj7ADURGpb34O42xW1%2Fsquidpy.svg?alt=media&amp;token=47cd8aa6-4b95-4df3-9939-1c4c16d8e92d">squidpy.svg</a></td></tr><tr><td><h3>muon</h3></td><td><span data-option="Ogvkay6aLgV0">Python Toolkit, </span><span data-option="8h3ulnwokyuU">Open Source</span></td><td><a href="https://github.com/scverse/muon" class="button primary medium" data-icon="github">GitHub</a><a href="https://muon.readthedocs.io/en/latest/index.html" class="button primary medium" data-icon="readme">Doc</a><a href="https://doi.org/10.1186/s13059-021-02577-8" class="button secondary medium" data-icon="up-right-from-square">Paper</a></td><td><ul><li><strong>Overview</strong>：A framework for multimodal and multi-sample omics data analysis, extending <code>AnnData</code> to <code>MuData</code> to conveniently handle containerized multi-modal or multi-sample datasets.</li><li><strong>How to Bridge</strong>：Directly ingests <code>.h5mu</code> files output by the <code>saw aggr</code> pipeline into <code>MuData</code> objects. Once loaded via m<em>uon</em>, you can seamlessly plug the dataset into <em>Scanpy</em>, <em>Squidpy</em>, and the broader scverse ecosystem for downstream clustering, visualization, and comparative analysis.</li></ul></td><td data-object-fit="contain"><a href="https://640092348-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FQfDAr8IqIRwkROXEczGT%2Fuploads%2FIt2AUYJr1aYE2GxQH5FG%2Fmuon.svg?alt=media&amp;token=e7550574-103a-435f-a618-ed7341f006c7">muon.svg</a></td></tr></tbody></table>