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Code for reproducing stLearn paper

Abstract:

Spatial Transcriptomics (ST) is an emerging technology that adds spatial dimensionality to the genome-wide transcriptional profiling of cells in undissociated tissues. From a sample, a typical ST technology can generate three spatial data types, namely morphological imaging, physical distance between spatial data points, and gene expression values. We developed three new algorithms, collectively referred to as stLearn, utilising all the three data types to study tissue biology. First, stLearn corrects for technical noise in spatial sequencing data by using tissue image features (optional), physical distance, and gene experession profiles (SME method). This way, stLearn can also impute missing data that can increase tissue coverage of spatial gene expression data. SME significantly improves clustering analysis to find specific spatial patterns not detectable by existing methods. Second, we present a new method, pseudo-time-space (PSTS), to model the spatiotemporal relationship of cellular transcriptional states across a tissue. Using immunofluorescence imaging and engineered mouse model of central nervous system injury, we validated PSTS results in reconstructing the spatial trajectory of microglia activation following brain insult. We also assessed the diagnostic potential of PSTS in studying breast cancer progression. Third, we developed a spatial interaction test, which integrates ligand-receptor expression and spatial neighbourhood information to find highly interactive regions within a tissue across thousands of ligand-receptor pairs. We thoroughly benchmark, assess false discovery, and validate the interactions in skin and breast cancer tissues. Together, the three algorithms that we developed, as implemented in the comprehensive and fast stLearn software, allow for the elucidation of biological processes within healthy and diseased tissues.

Main parts:

SME integrative analysis - Mapping cell types/

Spatio-temporal trajectories/

Cell-cell interactions/

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all
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(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
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;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
GitHub - BiomedicalMachineLearning/stlearn_manuscript: Codes for generating stlearn figures · GitHub
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Code for reproducing stLearn paper

Abstract:

Spatial Transcriptomics (ST) is an emerging technology that adds spatial dimensionality to the genome-wide transcriptional profiling of cells in undissociated tissues. From a sample, a typical ST technology can generate three spatial data types, namely morphological imaging, physical distance between spatial data points, and gene expression values. We developed three new algorithms, collectively referred to as stLearn, utilising all the three data types to study tissue biology. First, stLearn corrects for technical noise in spatial sequencing data by using tissue image features (optional), physical distance, and gene experession profiles (SME method). This way, stLearn can also impute missing data that can increase tissue coverage of spatial gene expression data. SME significantly improves clustering analysis to find specific spatial patterns not detectable by existing methods. Second, we present a new method, pseudo-time-space (PSTS), to model the spatiotemporal relationship of cellular transcriptional states across a tissue. Using immunofluorescence imaging and engineered mouse model of central nervous system injury, we validated PSTS results in reconstructing the spatial trajectory of microglia activation following brain insult. We also assessed the diagnostic potential of PSTS in studying breast cancer progression. Third, we developed a spatial interaction test, which integrates ligand-receptor expression and spatial neighbourhood information to find highly interactive regions within a tissue across thousands of ligand-receptor pairs. We thoroughly benchmark, assess false discovery, and validate the interactions in skin and breast cancer tissues. Together, the three algorithms that we developed, as implemented in the comprehensive and fast stLearn software, allow for the elucidation of biological processes within healthy and diseased tissues.

Main parts:

SME integrative analysis - Mapping cell types/

Spatio-temporal trajectories/

Cell-cell interactions/

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Codes for generating stlearn figures

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Force GitHub README to respect dark mode (function() { var style = document.createElement('style'); style.textContent = ' .markdown-body { color-scheme: dark light; } .markdown-body pre { background: #161b22 !important; } .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; } .markdown-body table th, .markdown-body table td { border-color: #30363d !important; } .markdown-body img { background: #0d1117; } .markdown-body blockquote { border-left-color: #8b949e; } .markdown-body hr { border-color: #30363d; } '; document.head.appendChild(style); })(); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - BiomedicalMachineLearning/stlearn_manuscript: Codes for generating stlearn figures · GitHub
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Code for reproducing stLearn paper

Abstract:

Spatial Transcriptomics (ST) is an emerging technology that adds spatial dimensionality to the genome-wide transcriptional profiling of cells in undissociated tissues. From a sample, a typical ST technology can generate three spatial data types, namely morphological imaging, physical distance between spatial data points, and gene expression values. We developed three new algorithms, collectively referred to as stLearn, utilising all the three data types to study tissue biology. First, stLearn corrects for technical noise in spatial sequencing data by using tissue image features (optional), physical distance, and gene experession profiles (SME method). This way, stLearn can also impute missing data that can increase tissue coverage of spatial gene expression data. SME significantly improves clustering analysis to find specific spatial patterns not detectable by existing methods. Second, we present a new method, pseudo-time-space (PSTS), to model the spatiotemporal relationship of cellular transcriptional states across a tissue. Using immunofluorescence imaging and engineered mouse model of central nervous system injury, we validated PSTS results in reconstructing the spatial trajectory of microglia activation following brain insult. We also assessed the diagnostic potential of PSTS in studying breast cancer progression. Third, we developed a spatial interaction test, which integrates ligand-receptor expression and spatial neighbourhood information to find highly interactive regions within a tissue across thousands of ligand-receptor pairs. We thoroughly benchmark, assess false discovery, and validate the interactions in skin and breast cancer tissues. Together, the three algorithms that we developed, as implemented in the comprehensive and fast stLearn software, allow for the elucidation of biological processes within healthy and diseased tissues.

Main parts:

SME integrative analysis - Mapping cell types/

Spatio-temporal trajectories/

Cell-cell interactions/

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Codes for generating stlearn figures

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Highlight search terms from Google/DuckDuckGo/Bing referrer (function() { var ref = document.referrer; var terms = []; if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) { var url = new URL(ref); var q = url.searchParams.get('q') || url.searchParams.get('p'); if (q) { terms = q.split(/\s+/).filter(function(t) { return t.length > 2; }); } } if (terms.length === 0) return; var style = document.createElement('style'); style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }'; document.head.appendChild(style); function highlight(node) { if (node.nodeType === 3) { // text node var text = node.textContent; var found = false; terms.forEach(function(term) { var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi'); if (regex.test(text)) { found = true; var frag = document.createDocumentFragment(); var parts = text.split(regex); parts.forEach(function(part, i) { if (i % 2 === 0) { frag.appendChild(document.createTextNode(part)); } else { var span = document.createElement('span'); span.className = 'userscript-highlight'; span.textContent = part; frag.appendChild(span); } }); node.parentNode.replaceChild(frag, node); } }); } else if (node.nodeType === 1 && node.childNodes) { // element var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT']; if (!skipTags.includes(node.tagName)) { Array.from(node.childNodes).forEach(highlight); } } } highlight(document.body); // Re-highlight on dynamic content var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1 || node.nodeType === 3) highlight(node); }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - BiomedicalMachineLearning/stlearn_manuscript: Codes for generating stlearn figures · GitHub
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Code for reproducing stLearn paper

Abstract:

Spatial Transcriptomics (ST) is an emerging technology that adds spatial dimensionality to the genome-wide transcriptional profiling of cells in undissociated tissues. From a sample, a typical ST technology can generate three spatial data types, namely morphological imaging, physical distance between spatial data points, and gene expression values. We developed three new algorithms, collectively referred to as stLearn, utilising all the three data types to study tissue biology. First, stLearn corrects for technical noise in spatial sequencing data by using tissue image features (optional), physical distance, and gene experession profiles (SME method). This way, stLearn can also impute missing data that can increase tissue coverage of spatial gene expression data. SME significantly improves clustering analysis to find specific spatial patterns not detectable by existing methods. Second, we present a new method, pseudo-time-space (PSTS), to model the spatiotemporal relationship of cellular transcriptional states across a tissue. Using immunofluorescence imaging and engineered mouse model of central nervous system injury, we validated PSTS results in reconstructing the spatial trajectory of microglia activation following brain insult. We also assessed the diagnostic potential of PSTS in studying breast cancer progression. Third, we developed a spatial interaction test, which integrates ligand-receptor expression and spatial neighbourhood information to find highly interactive regions within a tissue across thousands of ligand-receptor pairs. We thoroughly benchmark, assess false discovery, and validate the interactions in skin and breast cancer tissues. Together, the three algorithms that we developed, as implemented in the comprehensive and fast stLearn software, allow for the elucidation of biological processes within healthy and diseased tissues.

Main parts:

SME integrative analysis - Mapping cell types/

Spatio-temporal trajectories/

Cell-cell interactions/

About

Codes for generating stlearn figures

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Strip utm_, fbclid, gclid, etc. from all links on page (function() { var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content', 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid', 'ref', 'ref_src', 'source', 'medium', 'campaign']; function cleanUrl(url) { try { var u = new URL(url, window.location.origin); var changed = false; trackingParams.forEach(function(p) { if (u.searchParams.has(p)) { u.searchParams.delete(p); changed = true; } }); return changed ? u.toString() : url; } catch (e) { return url; } } function cleanLinks() { document.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } cleanLinks(); var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1) { if (node.tagName === 'A') cleanLinks(); node.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + ' GitHub - BiomedicalMachineLearning/stlearn_manuscript: Codes for generating stlearn figures · GitHub
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Code for reproducing stLearn paper

Abstract:

Spatial Transcriptomics (ST) is an emerging technology that adds spatial dimensionality to the genome-wide transcriptional profiling of cells in undissociated tissues. From a sample, a typical ST technology can generate three spatial data types, namely morphological imaging, physical distance between spatial data points, and gene expression values. We developed three new algorithms, collectively referred to as stLearn, utilising all the three data types to study tissue biology. First, stLearn corrects for technical noise in spatial sequencing data by using tissue image features (optional), physical distance, and gene experession profiles (SME method). This way, stLearn can also impute missing data that can increase tissue coverage of spatial gene expression data. SME significantly improves clustering analysis to find specific spatial patterns not detectable by existing methods. Second, we present a new method, pseudo-time-space (PSTS), to model the spatiotemporal relationship of cellular transcriptional states across a tissue. Using immunofluorescence imaging and engineered mouse model of central nervous system injury, we validated PSTS results in reconstructing the spatial trajectory of microglia activation following brain insult. We also assessed the diagnostic potential of PSTS in studying breast cancer progression. Third, we developed a spatial interaction test, which integrates ligand-receptor expression and spatial neighbourhood information to find highly interactive regions within a tissue across thousands of ligand-receptor pairs. We thoroughly benchmark, assess false discovery, and validate the interactions in skin and breast cancer tissues. Together, the three algorithms that we developed, as implemented in the comprehensive and fast stLearn software, allow for the elucidation of biological processes within healthy and diseased tissues.

Main parts:

SME integrative analysis - Mapping cell types/

Spatio-temporal trajectories/

Cell-cell interactions/

About

Codes for generating stlearn figures

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9 stars

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Auto-enable theater mode on YouTube (function() { function tryTheater() { var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]'); if (btn && !btn.classList.contains('activated')) { btn.click(); } } // Try immediately tryTheater(); // Try after navigation (SPA) var lastUrl = location.href; setInterval(function() { if (location.href !== lastUrl) { lastUrl = location.href; setTimeout(tryTheater, 500); } }, 1000); // Also try on player load var observer = new MutationObserver(tryTheater); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - BiomedicalMachineLearning/stlearn_manuscript: Codes for generating stlearn figures · GitHub
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Code for reproducing stLearn paper

Abstract:

Spatial Transcriptomics (ST) is an emerging technology that adds spatial dimensionality to the genome-wide transcriptional profiling of cells in undissociated tissues. From a sample, a typical ST technology can generate three spatial data types, namely morphological imaging, physical distance between spatial data points, and gene expression values. We developed three new algorithms, collectively referred to as stLearn, utilising all the three data types to study tissue biology. First, stLearn corrects for technical noise in spatial sequencing data by using tissue image features (optional), physical distance, and gene experession profiles (SME method). This way, stLearn can also impute missing data that can increase tissue coverage of spatial gene expression data. SME significantly improves clustering analysis to find specific spatial patterns not detectable by existing methods. Second, we present a new method, pseudo-time-space (PSTS), to model the spatiotemporal relationship of cellular transcriptional states across a tissue. Using immunofluorescence imaging and engineered mouse model of central nervous system injury, we validated PSTS results in reconstructing the spatial trajectory of microglia activation following brain insult. We also assessed the diagnostic potential of PSTS in studying breast cancer progression. Third, we developed a spatial interaction test, which integrates ligand-receptor expression and spatial neighbourhood information to find highly interactive regions within a tissue across thousands of ligand-receptor pairs. We thoroughly benchmark, assess false discovery, and validate the interactions in skin and breast cancer tissues. Together, the three algorithms that we developed, as implemented in the comprehensive and fast stLearn software, allow for the elucidation of biological processes within healthy and diseased tissues.

Main parts:

SME integrative analysis - Mapping cell types/

Spatio-temporal trajectories/

Cell-cell interactions/

About

Codes for generating stlearn figures

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9 stars

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Remove or un-stick sticky/fixed headers that block content (function() { function unstick() { document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) { if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') { el.style.position = 'static'; el.style.top = 'auto'; el.style.zIndex = 'auto'; } }); } unstick(); var observer = new MutationObserver(unstick); observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] }); })(); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - BiomedicalMachineLearning/stlearn_manuscript: Codes for generating stlearn figures · GitHub
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Code for reproducing stLearn paper

Abstract:

Spatial Transcriptomics (ST) is an emerging technology that adds spatial dimensionality to the genome-wide transcriptional profiling of cells in undissociated tissues. From a sample, a typical ST technology can generate three spatial data types, namely morphological imaging, physical distance between spatial data points, and gene expression values. We developed three new algorithms, collectively referred to as stLearn, utilising all the three data types to study tissue biology. First, stLearn corrects for technical noise in spatial sequencing data by using tissue image features (optional), physical distance, and gene experession profiles (SME method). This way, stLearn can also impute missing data that can increase tissue coverage of spatial gene expression data. SME significantly improves clustering analysis to find specific spatial patterns not detectable by existing methods. Second, we present a new method, pseudo-time-space (PSTS), to model the spatiotemporal relationship of cellular transcriptional states across a tissue. Using immunofluorescence imaging and engineered mouse model of central nervous system injury, we validated PSTS results in reconstructing the spatial trajectory of microglia activation following brain insult. We also assessed the diagnostic potential of PSTS in studying breast cancer progression. Third, we developed a spatial interaction test, which integrates ligand-receptor expression and spatial neighbourhood information to find highly interactive regions within a tissue across thousands of ligand-receptor pairs. We thoroughly benchmark, assess false discovery, and validate the interactions in skin and breast cancer tissues. Together, the three algorithms that we developed, as implemented in the comprehensive and fast stLearn software, allow for the elucidation of biological processes within healthy and diseased tissues.

Main parts:

SME integrative analysis - Mapping cell types/

Spatio-temporal trajectories/

Cell-cell interactions/

About

Codes for generating stlearn figures

Resources

Stars

9 stars

Watchers

1 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Universal Dark Mode - works on any site (function() { var enabled = true; function applyDarkMode() { if (!enabled) return; // Create style element if it doesn't exist var style = document.getElementById('universal-dark-mode-style'); if (!style) { style = document.createElement('style'); style.id = 'universal-dark-mode-style'; document.head.appendChild(style); } // Dark mode CSS - inverts colors but preserves images/video style.textContent = ' /* Invert everything except media */ html { filter: invert(1) hue-rotate(180deg) !important; background: #1a1a2e !important; } /* Restore images, videos, iframes, canvas */ img, video, iframe, canvas, svg, picture, [style*="background-image"] { filter: invert(1) hue-rotate(180deg) !important; } /* Preserve specific elements that should not be inverted */ .no-dark-mode, .no-dark-mode *, [data-theme="light"], [data-theme="light"], .ace_editor, .ace_editor *, .CodeMirror, .CodeMirror *, .monaco-editor, .monaco-editor *, .markdown-body pre, .markdown-body pre *, .highlight, .highlight *, pre code, pre code * { filter: none !important; } /* Fix common UI elements */ .modal, .popup, .dropdown-menu, .tooltip, .popover { filter: invert(1) hue-rotate(180deg) !important; background: #2d2d44 !important; border-color: #444 !important; } /* Scrollbars */ ::-webkit-scrollbar { background: #1a1a2e !important; } ::-webkit-scrollbar-thumb { background: #444 !important; } ::-webkit-scrollbar-thumb:hover { background: #555 !important; } /* Selection */ ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; } ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; } '; } function removeDarkMode() { var style = document.getElementById('universal-dark-mode-style'); if (style) style.remove(); } // Toggle with Alt+Shift+D document.addEventListener('keydown', function(e) { if (e.altKey && e.shiftKey && e.key === 'D') { e.preventDefault(); enabled = !enabled; if (enabled) { applyDarkMode(); console.log('[Universal Dark Mode] Enabled'); } else { removeDarkMode(); console.log('[Universal Dark Mode] Disabled'); } } }); // Apply on load applyDarkMode(); // Re-apply on dynamic content var observer = new MutationObserver(function(mutations) { if (enabled && !document.getElementById('universal-dark-mode-style')) { applyDarkMode(); } }); observer.observe(document.head, { childList: true }); console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle'); })(); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })(); GitHub - BiomedicalMachineLearning/stlearn_manuscript: Codes for generating stlearn figures · GitHub
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Code for reproducing stLearn paper

Abstract:

Spatial Transcriptomics (ST) is an emerging technology that adds spatial dimensionality to the genome-wide transcriptional profiling of cells in undissociated tissues. From a sample, a typical ST technology can generate three spatial data types, namely morphological imaging, physical distance between spatial data points, and gene expression values. We developed three new algorithms, collectively referred to as stLearn, utilising all the three data types to study tissue biology. First, stLearn corrects for technical noise in spatial sequencing data by using tissue image features (optional), physical distance, and gene experession profiles (SME method). This way, stLearn can also impute missing data that can increase tissue coverage of spatial gene expression data. SME significantly improves clustering analysis to find specific spatial patterns not detectable by existing methods. Second, we present a new method, pseudo-time-space (PSTS), to model the spatiotemporal relationship of cellular transcriptional states across a tissue. Using immunofluorescence imaging and engineered mouse model of central nervous system injury, we validated PSTS results in reconstructing the spatial trajectory of microglia activation following brain insult. We also assessed the diagnostic potential of PSTS in studying breast cancer progression. Third, we developed a spatial interaction test, which integrates ligand-receptor expression and spatial neighbourhood information to find highly interactive regions within a tissue across thousands of ligand-receptor pairs. We thoroughly benchmark, assess false discovery, and validate the interactions in skin and breast cancer tissues. Together, the three algorithms that we developed, as implemented in the comprehensive and fast stLearn software, allow for the elucidation of biological processes within healthy and diseased tissues.

Main parts:

SME integrative analysis - Mapping cell types/

Spatio-temporal trajectories/

Cell-cell interactions/

About

Codes for generating stlearn figures

Resources

Stars

9 stars

Watchers

1 watching

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