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STIM - the Spatial Transcriptomics as Images Project

Example rendering of calm-2, ptgds, mbp

The Spatial Transcriptomics as Images Project (STIM) is a framework for storing, (interactively) viewing, aligning, and processing spatial transcriptomics data, which builds on the powerful libraries Imglib2, N5, BigDataViewer and Fiji.

STIM can be installed through Conda and uses AnnData and/or N5 as a storage backend thus allowing easy interaction with existing tools for spatial transcriptomics. It provides efficient access to spatial transcriptomics data "classically" as values, or can render them as images at arbitrary resolution. These image representations allow to apply computer vision techniques to spatially resolved sequencing datasets.

Some highlights are:

  • efficient interactive rendering (using BigDataViewer)
  • rendering high-quality still images (using ImageJ/Fiji)
  • alignment of spatial dataset slides using SIFT, ICP and RANSAC combined with global optimization
  • efficient and fast storage using AnnData and N5 for multi-slice datasets
  • Conda installable, straight-forward interoperability with other packages
  • image filtering framework for irregularly-spaced datasets

A great example dataset is provided by the SlideSeq paper and can be downloaded here.

A minimal example of a two-slice Visium dataset is available here. We provide a detailed walk-through for this dataset in the wiki to get you started.Note: we highly recommend this tutorial as a starting point for using STIM. If you have any questions, feature requests or concerns please open an issue here on GitHub.

Please see the wiki for additional tutorials, detailed installation instructions, and more.

Installation

We recommend using Conda to install STIM. If you don't have Conda installed, please follow the instructions here. Once Conda is installed, you can install STIM from conda-forge by running:

conda install -c conda-forge stim

You can also build STIM from source, see the detailed instructions in the wiki.

Cite STIM

If you use STIM in your research, please cite our publication:

Preibisch, S., Innerberger, M., Leon-Perinan, D., Karaiskos, N., & Rajewsky, N. (2024). Scalable image-based visualization and alignment of spatial transcriptomics datasets. Cell Systems, Vol. 16, No. 5 (2025).

Wrapping in Python

The stimwrap package from the Rajewsky lab provides an interface to extract datasets and their attributes from an N5-container created by STIM. The package can be easily installed via pip:

pip install stimwrap

Java code examples

There are example classes in this Java package that can help you to get started with programming in STIM, specifically VisualizeStack.java and TestDisplayModes.java could be useful. Additionally, the code for all command-line tools can be found here, which is also a good starting place in combination with the tutorials. Note that the install scripts install and install_windows.bat show a link between the command-line tool name and Java class name, e.g., st-bdv-view is cmd.DisplayStackedSlides.

About

Library for managing, storage, viewing, and working with spatial transcriptomics data using Imglib2, N5, BigDataViewer and Fiji.

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GPL-3.0 license
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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STIM - the Spatial Transcriptomics as Images Project
Example rendering of calm-2, ptgds, mbp
The Spatial Transcriptomics as Images Project (STIM) is a framework for storing, (interactively) viewing, aligning, and processing spatial transcriptomics data, which builds on the powerful libraries Imglib2, N5, BigDataViewer and Fiji.
STIM can be installed through Conda and uses AnnData and/or N5 as a storage backend thus allowing easy interaction with existing tools for spatial transcriptomics. It provides efficient access to spatial transcriptomics data "classically" as values, or can render them as images at arbitrary resolution. These image representations allow to apply computer vision techniques to spatially resolved sequencing datasets.
Some highlights are:
  • efficient interactive rendering (using BigDataViewer)
  • rendering high-quality still images (using ImageJ/Fiji)
  • alignment of spatial dataset slides using SIFT, ICP and RANSAC combined with global optimization
  • efficient and fast storage using AnnData and N5 for multi-slice datasets
  • Conda installable, straight-forward interoperability with other packages
  • image filtering framework for irregularly-spaced datasets
A great example dataset is provided by the SlideSeq paper and can be downloaded here.
A minimal example of a two-slice Visium dataset is available here.
We provide a detailed walk-through for this dataset in the wiki to get you started.Note: we highly recommend this tutorial as a starting point for using STIM. If you have any questions, feature requests or concerns please open an issue here on GitHub.
Please see the wiki for additional tutorials, detailed installation instructions, and more.
Installation
We recommend using Conda to install STIM. If you don't have Conda installed, please follow the instructions here. Once Conda is installed, you can install STIM from conda-forge by running:
conda install -c conda-forge stim
You can also build STIM from source, see the detailed instructions in the wiki.
Cite STIM
If you use STIM in your research, please cite our publication:
Preibisch, S., Innerberger, M., Leon-Perinan, D., Karaiskos, N., & Rajewsky, N. (2024). Scalable image-based visualization and alignment of spatial transcriptomics datasets. Cell Systems, Vol. 16, No. 5 (2025).
Wrapping in Python
The stimwrap package from the Rajewsky lab provides an interface to extract datasets and their attributes from an N5-container created by STIM.
The package can be easily installed via pip:
pip install stimwrap
Java code examples
There are example classes in this Java package that can help you to get started with programming in STIM, specifically VisualizeStack.java and TestDisplayModes.java could be useful.
Additionally, the code for all command-line tools can be found here, which is also a good starting place in combination with the tutorials.
Note that the install scripts install and install_windows.bat show a link between the command-line tool name and Java class name, e.g., st-bdv-view is cmd.DisplayStackedSlides.
About
Library for managing, storage, viewing, and working with spatial transcriptomics data using Imglib2, N5, BigDataViewer and Fiji.
Resources
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GPL-3.0 license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
STIM - the Spatial Transcriptomics as Images Project
Example rendering of calm-2, ptgds, mbp
The Spatial Transcriptomics as Images Project (STIM) is a framework for storing, (interactively) viewing, aligning, and processing spatial transcriptomics data, which builds on the powerful libraries Imglib2, N5, BigDataViewer and Fiji.
STIM can be installed through Conda and uses AnnData and/or N5 as a storage backend thus allowing easy interaction with existing tools for spatial transcriptomics. It provides efficient access to spatial transcriptomics data "classically" as values, or can render them as images at arbitrary resolution. These image representations allow to apply computer vision techniques to spatially resolved sequencing datasets.
Some highlights are:
  • efficient interactive rendering (using BigDataViewer)
  • rendering high-quality still images (using ImageJ/Fiji)
  • alignment of spatial dataset slides using SIFT, ICP and RANSAC combined with global optimization
  • efficient and fast storage using AnnData and N5 for multi-slice datasets
  • Conda installable, straight-forward interoperability with other packages
  • image filtering framework for irregularly-spaced datasets
A great example dataset is provided by the SlideSeq paper and can be downloaded here.
A minimal example of a two-slice Visium dataset is available here.
We provide a detailed walk-through for this dataset in the wiki to get you started.Note: we highly recommend this tutorial as a starting point for using STIM. If you have any questions, feature requests or concerns please open an issue here on GitHub.
Please see the wiki for additional tutorials, detailed installation instructions, and more.
Installation
We recommend using Conda to install STIM. If you don't have Conda installed, please follow the instructions here. Once Conda is installed, you can install STIM from conda-forge by running:
conda install -c conda-forge stim
You can also build STIM from source, see the detailed instructions in the wiki.
Cite STIM
If you use STIM in your research, please cite our publication:
Preibisch, S., Innerberger, M., Leon-Perinan, D., Karaiskos, N., & Rajewsky, N. (2024). Scalable image-based visualization and alignment of spatial transcriptomics datasets. Cell Systems, Vol. 16, No. 5 (2025).
Wrapping in Python
The stimwrap package from the Rajewsky lab provides an interface to extract datasets and their attributes from an N5-container created by STIM.
The package can be easily installed via pip:
pip install stimwrap
Java code examples
There are example classes in this Java package that can help you to get started with programming in STIM, specifically VisualizeStack.java and TestDisplayModes.java could be useful.
Additionally, the code for all command-line tools can be found here, which is also a good starting place in combination with the tutorials.
Note that the install scripts install and install_windows.bat show a link between the command-line tool name and Java class name, e.g., st-bdv-view is cmd.DisplayStackedSlides.
About
Library for managing, storage, viewing, and working with spatial transcriptomics data using Imglib2, N5, BigDataViewer and Fiji.
Resources
Readme
GPL-3.0 license
Activity
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Stars
32 stars
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12 watching
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Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
STIM - the Spatial Transcriptomics as Images Project
Example rendering of calm-2, ptgds, mbp
The Spatial Transcriptomics as Images Project (STIM) is a framework for storing, (interactively) viewing, aligning, and processing spatial transcriptomics data, which builds on the powerful libraries Imglib2, N5, BigDataViewer and Fiji.
STIM can be installed through Conda and uses AnnData and/or N5 as a storage backend thus allowing easy interaction with existing tools for spatial transcriptomics. It provides efficient access to spatial transcriptomics data "classically" as values, or can render them as images at arbitrary resolution. These image representations allow to apply computer vision techniques to spatially resolved sequencing datasets.
Some highlights are:
  • efficient interactive rendering (using BigDataViewer)
  • rendering high-quality still images (using ImageJ/Fiji)
  • alignment of spatial dataset slides using SIFT, ICP and RANSAC combined with global optimization
  • efficient and fast storage using AnnData and N5 for multi-slice datasets
  • Conda installable, straight-forward interoperability with other packages
  • image filtering framework for irregularly-spaced datasets
A great example dataset is provided by the SlideSeq paper and can be downloaded here.
A minimal example of a two-slice Visium dataset is available here.
We provide a detailed walk-through for this dataset in the wiki to get you started.Note: we highly recommend this tutorial as a starting point for using STIM. If you have any questions, feature requests or concerns please open an issue here on GitHub.
Please see the wiki for additional tutorials, detailed installation instructions, and more.
Installation
We recommend using Conda to install STIM. If you don't have Conda installed, please follow the instructions here. Once Conda is installed, you can install STIM from conda-forge by running:
conda install -c conda-forge stim
You can also build STIM from source, see the detailed instructions in the wiki.
Cite STIM
If you use STIM in your research, please cite our publication:
Preibisch, S., Innerberger, M., Leon-Perinan, D., Karaiskos, N., & Rajewsky, N. (2024). Scalable image-based visualization and alignment of spatial transcriptomics datasets. Cell Systems, Vol. 16, No. 5 (2025).
Wrapping in Python
The stimwrap package from the Rajewsky lab provides an interface to extract datasets and their attributes from an N5-container created by STIM.
The package can be easily installed via pip:
pip install stimwrap
Java code examples
There are example classes in this Java package that can help you to get started with programming in STIM, specifically VisualizeStack.java and TestDisplayModes.java could be useful.
Additionally, the code for all command-line tools can be found here, which is also a good starting place in combination with the tutorials.
Note that the install scripts install and install_windows.bat show a link between the command-line tool name and Java class name, e.g., st-bdv-view is cmd.DisplayStackedSlides.
About
Library for managing, storage, viewing, and working with spatial transcriptomics data using Imglib2, N5, BigDataViewer and Fiji.
Resources
Readme
GPL-3.0 license
Activity
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Stars
32 stars
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12 watching
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1 fork
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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STIM - the Spatial Transcriptomics as Images Project
Example rendering of calm-2, ptgds, mbp
The Spatial Transcriptomics as Images Project (STIM) is a framework for storing, (interactively) viewing, aligning, and processing spatial transcriptomics data, which builds on the powerful libraries Imglib2, N5, BigDataViewer and Fiji.
STIM can be installed through Conda and uses AnnData and/or N5 as a storage backend thus allowing easy interaction with existing tools for spatial transcriptomics. It provides efficient access to spatial transcriptomics data "classically" as values, or can render them as images at arbitrary resolution. These image representations allow to apply computer vision techniques to spatially resolved sequencing datasets.
Some highlights are:
  • efficient interactive rendering (using BigDataViewer)
  • rendering high-quality still images (using ImageJ/Fiji)
  • alignment of spatial dataset slides using SIFT, ICP and RANSAC combined with global optimization
  • efficient and fast storage using AnnData and N5 for multi-slice datasets
  • Conda installable, straight-forward interoperability with other packages
  • image filtering framework for irregularly-spaced datasets
A great example dataset is provided by the SlideSeq paper and can be downloaded here.
A minimal example of a two-slice Visium dataset is available here.
We provide a detailed walk-through for this dataset in the wiki to get you started.Note: we highly recommend this tutorial as a starting point for using STIM. If you have any questions, feature requests or concerns please open an issue here on GitHub.
Please see the wiki for additional tutorials, detailed installation instructions, and more.
Installation
We recommend using Conda to install STIM. If you don't have Conda installed, please follow the instructions here. Once Conda is installed, you can install STIM from conda-forge by running:
conda install -c conda-forge stim
You can also build STIM from source, see the detailed instructions in the wiki.
Cite STIM
If you use STIM in your research, please cite our publication:
Preibisch, S., Innerberger, M., Leon-Perinan, D., Karaiskos, N., & Rajewsky, N. (2024). Scalable image-based visualization and alignment of spatial transcriptomics datasets. Cell Systems, Vol. 16, No. 5 (2025).
Wrapping in Python
The stimwrap package from the Rajewsky lab provides an interface to extract datasets and their attributes from an N5-container created by STIM.
The package can be easily installed via pip:
pip install stimwrap
Java code examples
There are example classes in this Java package that can help you to get started with programming in STIM, specifically VisualizeStack.java and TestDisplayModes.java could be useful.
Additionally, the code for all command-line tools can be found here, which is also a good starting place in combination with the tutorials.
Note that the install scripts install and install_windows.bat show a link between the command-line tool name and Java class name, e.g., st-bdv-view is cmd.DisplayStackedSlides.
About
Library for managing, storage, viewing, and working with spatial transcriptomics data using Imglib2, N5, BigDataViewer and Fiji.
Resources
Readme
GPL-3.0 license
Activity
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
STIM - the Spatial Transcriptomics as Images Project
Example rendering of calm-2, ptgds, mbp
The Spatial Transcriptomics as Images Project (STIM) is a framework for storing, (interactively) viewing, aligning, and processing spatial transcriptomics data, which builds on the powerful libraries Imglib2, N5, BigDataViewer and Fiji.
STIM can be installed through Conda and uses AnnData and/or N5 as a storage backend thus allowing easy interaction with existing tools for spatial transcriptomics. It provides efficient access to spatial transcriptomics data "classically" as values, or can render them as images at arbitrary resolution. These image representations allow to apply computer vision techniques to spatially resolved sequencing datasets.
Some highlights are:
  • efficient interactive rendering (using BigDataViewer)
  • rendering high-quality still images (using ImageJ/Fiji)
  • alignment of spatial dataset slides using SIFT, ICP and RANSAC combined with global optimization
  • efficient and fast storage using AnnData and N5 for multi-slice datasets
  • Conda installable, straight-forward interoperability with other packages
  • image filtering framework for irregularly-spaced datasets
A great example dataset is provided by the SlideSeq paper and can be downloaded here.
A minimal example of a two-slice Visium dataset is available here.
We provide a detailed walk-through for this dataset in the wiki to get you started.Note: we highly recommend this tutorial as a starting point for using STIM. If you have any questions, feature requests or concerns please open an issue here on GitHub.
Please see the wiki for additional tutorials, detailed installation instructions, and more.
Installation
We recommend using Conda to install STIM. If you don't have Conda installed, please follow the instructions here. Once Conda is installed, you can install STIM from conda-forge by running:
conda install -c conda-forge stim
You can also build STIM from source, see the detailed instructions in the wiki.
Cite STIM
If you use STIM in your research, please cite our publication:
Preibisch, S., Innerberger, M., Leon-Perinan, D., Karaiskos, N., & Rajewsky, N. (2024). Scalable image-based visualization and alignment of spatial transcriptomics datasets. Cell Systems, Vol. 16, No. 5 (2025).
Wrapping in Python
The stimwrap package from the Rajewsky lab provides an interface to extract datasets and their attributes from an N5-container created by STIM.
The package can be easily installed via pip:
pip install stimwrap
Java code examples
There are example classes in this Java package that can help you to get started with programming in STIM, specifically VisualizeStack.java and TestDisplayModes.java could be useful.
Additionally, the code for all command-line tools can be found here, which is also a good starting place in combination with the tutorials.
Note that the install scripts install and install_windows.bat show a link between the command-line tool name and Java class name, e.g., st-bdv-view is cmd.DisplayStackedSlides.
About
Library for managing, storage, viewing, and working with spatial transcriptomics data using Imglib2, N5, BigDataViewer and Fiji.
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
32 stars
Watchers
12 watching
Forks
1 fork
Report repository
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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STIM - the Spatial Transcriptomics as Images Project
Example rendering of calm-2, ptgds, mbp
The Spatial Transcriptomics as Images Project (STIM) is a framework for storing, (interactively) viewing, aligning, and processing spatial transcriptomics data, which builds on the powerful libraries Imglib2, N5, BigDataViewer and Fiji.
STIM can be installed through Conda and uses AnnData and/or N5 as a storage backend thus allowing easy interaction with existing tools for spatial transcriptomics. It provides efficient access to spatial transcriptomics data "classically" as values, or can render them as images at arbitrary resolution. These image representations allow to apply computer vision techniques to spatially resolved sequencing datasets.
Some highlights are:
  • efficient interactive rendering (using BigDataViewer)
  • rendering high-quality still images (using ImageJ/Fiji)
  • alignment of spatial dataset slides using SIFT, ICP and RANSAC combined with global optimization
  • efficient and fast storage using AnnData and N5 for multi-slice datasets
  • Conda installable, straight-forward interoperability with other packages
  • image filtering framework for irregularly-spaced datasets
A great example dataset is provided by the SlideSeq paper and can be downloaded here.
A minimal example of a two-slice Visium dataset is available here.
We provide a detailed walk-through for this dataset in the wiki to get you started.Note: we highly recommend this tutorial as a starting point for using STIM. If you have any questions, feature requests or concerns please open an issue here on GitHub.
Please see the wiki for additional tutorials, detailed installation instructions, and more.
Installation
We recommend using Conda to install STIM. If you don't have Conda installed, please follow the instructions here. Once Conda is installed, you can install STIM from conda-forge by running:
conda install -c conda-forge stim
You can also build STIM from source, see the detailed instructions in the wiki.
Cite STIM
If you use STIM in your research, please cite our publication:
Preibisch, S., Innerberger, M., Leon-Perinan, D., Karaiskos, N., & Rajewsky, N. (2024). Scalable image-based visualization and alignment of spatial transcriptomics datasets. Cell Systems, Vol. 16, No. 5 (2025).
Wrapping in Python
The stimwrap package from the Rajewsky lab provides an interface to extract datasets and their attributes from an N5-container created by STIM.
The package can be easily installed via pip:
pip install stimwrap
Java code examples
There are example classes in this Java package that can help you to get started with programming in STIM, specifically VisualizeStack.java and TestDisplayModes.java could be useful.
Additionally, the code for all command-line tools can be found here, which is also a good starting place in combination with the tutorials.
Note that the install scripts install and install_windows.bat show a link between the command-line tool name and Java class name, e.g., st-bdv-view is cmd.DisplayStackedSlides.
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Library for managing, storage, viewing, and working with spatial transcriptomics data using Imglib2, N5, BigDataViewer and Fiji.
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if (__m === '*' || __re.test(location.href)) {
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Repository files navigation
STIM - the Spatial Transcriptomics as Images Project
Example rendering of calm-2, ptgds, mbp
The Spatial Transcriptomics as Images Project (STIM) is a framework for storing, (interactively) viewing, aligning, and processing spatial transcriptomics data, which builds on the powerful libraries Imglib2, N5, BigDataViewer and Fiji.
STIM can be installed through Conda and uses AnnData and/or N5 as a storage backend thus allowing easy interaction with existing tools for spatial transcriptomics. It provides efficient access to spatial transcriptomics data "classically" as values, or can render them as images at arbitrary resolution. These image representations allow to apply computer vision techniques to spatially resolved sequencing datasets.
Some highlights are:
  • efficient interactive rendering (using BigDataViewer)
  • rendering high-quality still images (using ImageJ/Fiji)
  • alignment of spatial dataset slides using SIFT, ICP and RANSAC combined with global optimization
  • efficient and fast storage using AnnData and N5 for multi-slice datasets
  • Conda installable, straight-forward interoperability with other packages
  • image filtering framework for irregularly-spaced datasets
A great example dataset is provided by the SlideSeq paper and can be downloaded here.
A minimal example of a two-slice Visium dataset is available here.
We provide a detailed walk-through for this dataset in the wiki to get you started.Note: we highly recommend this tutorial as a starting point for using STIM. If you have any questions, feature requests or concerns please open an issue here on GitHub.
Please see the wiki for additional tutorials, detailed installation instructions, and more.
Installation
We recommend using Conda to install STIM. If you don't have Conda installed, please follow the instructions here. Once Conda is installed, you can install STIM from conda-forge by running:
conda install -c conda-forge stim
You can also build STIM from source, see the detailed instructions in the wiki.
Cite STIM
If you use STIM in your research, please cite our publication:
Preibisch, S., Innerberger, M., Leon-Perinan, D., Karaiskos, N., & Rajewsky, N. (2024). Scalable image-based visualization and alignment of spatial transcriptomics datasets. Cell Systems, Vol. 16, No. 5 (2025).
Wrapping in Python
The stimwrap package from the Rajewsky lab provides an interface to extract datasets and their attributes from an N5-container created by STIM.
The package can be easily installed via pip:
pip install stimwrap
Java code examples
There are example classes in this Java package that can help you to get started with programming in STIM, specifically VisualizeStack.java and TestDisplayModes.java could be useful.
Additionally, the code for all command-line tools can be found here, which is also a good starting place in combination with the tutorials.
Note that the install scripts install and install_windows.bat show a link between the command-line tool name and Java class name, e.g., st-bdv-view is cmd.DisplayStackedSlides.
About
Library for managing, storage, viewing, and working with spatial transcriptomics data using Imglib2, N5, BigDataViewer and Fiji.
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
32 stars
Watchers
12 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages