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FISHFactor: A Probabilistic Factor Model for Spatial Transcriptomics Data with Subcellular Resolution

Code repository supplementing the paper.

FISHFactor is a non-negative, spatially informed factor analysis model with a Poisson point process likelihood to model single-molecule resolved data, as obtained for example from multiplexed fluorescence in-situ hybridization methods. In addition, FISHFactor allows to integrate multiple cells by jointly inferring cell-specific factors and a weight matrix that is shared between cells. The model is implemented using the deep probabilistic programming language Pyro and the Gaussian process package GPyTorch.

Repository structure

  • src/ contains the FISHFactor model, data simulation and util functions.
  • experiments/ contains the experiments described in the paper.
  • data/ contains scripts to download and process data used in the paper.

Usage

The required packages can be installed in an Anaconda environment using the environment.yml file. An example for using FISHFactor with simulated cells is shown in fishfactor_demo.ipynb.

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A probabilistic factor model for spatial transcriptomics data with subcellular resolution

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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FISHFactor: A Probabilistic Factor Model for Spatial Transcriptomics Data with Subcellular Resolution
Code repository supplementing the paper.
FISHFactor is a non-negative, spatially informed factor analysis model with a Poisson point process likelihood to model single-molecule resolved data, as obtained for example from multiplexed fluorescence in-situ hybridization methods. In addition, FISHFactor allows to integrate multiple cells by jointly inferring cell-specific factors and a weight matrix that is shared between cells. The model is implemented using the deep probabilistic programming language Pyro and the Gaussian process package GPyTorch.
Repository structure
  • src/ contains the FISHFactor model, data simulation and util functions.
  • experiments/ contains the experiments described in the paper.
  • data/ contains scripts to download and process data used in the paper.
Usage
The required packages can be installed in an Anaconda environment using the environment.yml file. An example for using FISHFactor with simulated cells is shown in fishfactor_demo.ipynb.
About
A probabilistic factor model for spatial transcriptomics data with subcellular resolution
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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");
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})();
(function(){
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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FISHFactor: A Probabilistic Factor Model for Spatial Transcriptomics Data with Subcellular Resolution
Code repository supplementing the paper.
FISHFactor is a non-negative, spatially informed factor analysis model with a Poisson point process likelihood to model single-molecule resolved data, as obtained for example from multiplexed fluorescence in-situ hybridization methods. In addition, FISHFactor allows to integrate multiple cells by jointly inferring cell-specific factors and a weight matrix that is shared between cells. The model is implemented using the deep probabilistic programming language Pyro and the Gaussian process package GPyTorch.
Repository structure
  • src/ contains the FISHFactor model, data simulation and util functions.
  • experiments/ contains the experiments described in the paper.
  • data/ contains scripts to download and process data used in the paper.
Usage
The required packages can be installed in an Anaconda environment using the environment.yml file. An example for using FISHFactor with simulated cells is shown in fishfactor_demo.ipynb.
About
A probabilistic factor model for spatial transcriptomics data with subcellular resolution
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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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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FISHFactor: A Probabilistic Factor Model for Spatial Transcriptomics Data with Subcellular Resolution
Code repository supplementing the paper.
FISHFactor is a non-negative, spatially informed factor analysis model with a Poisson point process likelihood to model single-molecule resolved data, as obtained for example from multiplexed fluorescence in-situ hybridization methods. In addition, FISHFactor allows to integrate multiple cells by jointly inferring cell-specific factors and a weight matrix that is shared between cells. The model is implemented using the deep probabilistic programming language Pyro and the Gaussian process package GPyTorch.
Repository structure
  • src/ contains the FISHFactor model, data simulation and util functions.
  • experiments/ contains the experiments described in the paper.
  • data/ contains scripts to download and process data used in the paper.
Usage
The required packages can be installed in an Anaconda environment using the environment.yml file. An example for using FISHFactor with simulated cells is shown in fishfactor_demo.ipynb.
About
A probabilistic factor model for spatial transcriptomics data with subcellular resolution
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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");
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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FISHFactor: A Probabilistic Factor Model for Spatial Transcriptomics Data with Subcellular Resolution
Code repository supplementing the paper.
FISHFactor is a non-negative, spatially informed factor analysis model with a Poisson point process likelihood to model single-molecule resolved data, as obtained for example from multiplexed fluorescence in-situ hybridization methods. In addition, FISHFactor allows to integrate multiple cells by jointly inferring cell-specific factors and a weight matrix that is shared between cells. The model is implemented using the deep probabilistic programming language Pyro and the Gaussian process package GPyTorch.
Repository structure
  • src/ contains the FISHFactor model, data simulation and util functions.
  • experiments/ contains the experiments described in the paper.
  • data/ contains scripts to download and process data used in the paper.
Usage
The required packages can be installed in an Anaconda environment using the environment.yml file. An example for using FISHFactor with simulated cells is shown in fishfactor_demo.ipynb.
About
A probabilistic factor model for spatial transcriptomics data with subcellular resolution
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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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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FISHFactor: A Probabilistic Factor Model for Spatial Transcriptomics Data with Subcellular Resolution
Code repository supplementing the paper.
FISHFactor is a non-negative, spatially informed factor analysis model with a Poisson point process likelihood to model single-molecule resolved data, as obtained for example from multiplexed fluorescence in-situ hybridization methods. In addition, FISHFactor allows to integrate multiple cells by jointly inferring cell-specific factors and a weight matrix that is shared between cells. The model is implemented using the deep probabilistic programming language Pyro and the Gaussian process package GPyTorch.
Repository structure
  • src/ contains the FISHFactor model, data simulation and util functions.
  • experiments/ contains the experiments described in the paper.
  • data/ contains scripts to download and process data used in the paper.
Usage
The required packages can be installed in an Anaconda environment using the environment.yml file. An example for using FISHFactor with simulated cells is shown in fishfactor_demo.ipynb.
About
A probabilistic factor model for spatial transcriptomics data with subcellular resolution
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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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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FISHFactor: A Probabilistic Factor Model for Spatial Transcriptomics Data with Subcellular Resolution
Code repository supplementing the paper.
FISHFactor is a non-negative, spatially informed factor analysis model with a Poisson point process likelihood to model single-molecule resolved data, as obtained for example from multiplexed fluorescence in-situ hybridization methods. In addition, FISHFactor allows to integrate multiple cells by jointly inferring cell-specific factors and a weight matrix that is shared between cells. The model is implemented using the deep probabilistic programming language Pyro and the Gaussian process package GPyTorch.
Repository structure
  • src/ contains the FISHFactor model, data simulation and util functions.
  • experiments/ contains the experiments described in the paper.
  • data/ contains scripts to download and process data used in the paper.
Usage
The required packages can be installed in an Anaconda environment using the environment.yml file. An example for using FISHFactor with simulated cells is shown in fishfactor_demo.ipynb.
About
A probabilistic factor model for spatial transcriptomics data with subcellular resolution
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if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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FISHFactor: A Probabilistic Factor Model for Spatial Transcriptomics Data with Subcellular Resolution
Code repository supplementing the paper.
FISHFactor is a non-negative, spatially informed factor analysis model with a Poisson point process likelihood to model single-molecule resolved data, as obtained for example from multiplexed fluorescence in-situ hybridization methods. In addition, FISHFactor allows to integrate multiple cells by jointly inferring cell-specific factors and a weight matrix that is shared between cells. The model is implemented using the deep probabilistic programming language Pyro and the Gaussian process package GPyTorch.
Repository structure
  • src/ contains the FISHFactor model, data simulation and util functions.
  • experiments/ contains the experiments described in the paper.
  • data/ contains scripts to download and process data used in the paper.
Usage
The required packages can be installed in an Anaconda environment using the environment.yml file. An example for using FISHFactor with simulated cells is shown in fishfactor_demo.ipynb.
About
A probabilistic factor model for spatial transcriptomics data with subcellular resolution
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