Skip to content

Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types #347

Description

@meisproject

Thank you for developing such an excellent software tool!

I'm currently using stlearn for CCI analysis on Visium HD data, and I'm only interested in a subset of specific cell types.

I have a question about the optimal workflow: Should I filter the AnnData object to retain only my cell types of interest before performing CCI analysis (note that these cell types may not be spatially contiguous), or should I follow the Xenium tutorial approach by performing CCI analysis on all cells after gridding, and then filter for my cell types of interest only during the visualization/plotting stage?

I would greatly appreciate your guidance on the recommended best practice for this scenario.
Thank you in advance for your time and help!

Metadata

Metadata

Assignees

No one assigned

    Labels

    No labels
    No labels

    Projects

    No projects

      Milestone

      No milestone

      Relationships

      None yet

      Development

      No branches or pull requests

      Issue actions

      , 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all
       blocks
      (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" + '
      Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types · Issue #347 · BiomedicalMachineLearning/stLearn · GitHub
      Skip to content

      Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types #347

      Description

      @meisproject

      Thank you for developing such an excellent software tool!

      I'm currently using stlearn for CCI analysis on Visium HD data, and I'm only interested in a subset of specific cell types.

      I have a question about the optimal workflow: Should I filter the AnnData object to retain only my cell types of interest before performing CCI analysis (note that these cell types may not be spatially contiguous), or should I follow the Xenium tutorial approach by performing CCI analysis on all cells after gridding, and then filter for my cell types of interest only during the visualization/plotting stage?

      I would greatly appreciate your guidance on the recommended best practice for this scenario.
      Thank you in advance for your time and help!

      Metadata

      Metadata

      Assignees

      No one assigned

        Labels

        No labels
        No labels

        Projects

        No projects

          Milestone

          No milestone

          Relationships

          None yet

          Development

          No branches or pull requests

          Issue actions

          , '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('^' + ".*" + ' Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types · Issue #347 · BiomedicalMachineLearning/stLearn · GitHub
          Skip to content

          Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types #347

          Description

          @meisproject

          Thank you for developing such an excellent software tool!

          I'm currently using stlearn for CCI analysis on Visium HD data, and I'm only interested in a subset of specific cell types.

          I have a question about the optimal workflow: Should I filter the AnnData object to retain only my cell types of interest before performing CCI analysis (note that these cell types may not be spatially contiguous), or should I follow the Xenium tutorial approach by performing CCI analysis on all cells after gridding, and then filter for my cell types of interest only during the visualization/plotting stage?

          I would greatly appreciate your guidance on the recommended best practice for this scenario.
          Thank you in advance for your time and help!

          Metadata

          Metadata

          Assignees

          No one assigned

            Labels

            No labels
            No labels

            Projects

            No projects

              Milestone

              No milestone

              Relationships

              None yet

              Development

              No branches or pull requests

              Issue actions

              , '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('^' + ".*" + ' Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types · Issue #347 · BiomedicalMachineLearning/stLearn · GitHub
              Skip to content

              Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types #347

              Description

              @meisproject

              Thank you for developing such an excellent software tool!

              I'm currently using stlearn for CCI analysis on Visium HD data, and I'm only interested in a subset of specific cell types.

              I have a question about the optimal workflow: Should I filter the AnnData object to retain only my cell types of interest before performing CCI analysis (note that these cell types may not be spatially contiguous), or should I follow the Xenium tutorial approach by performing CCI analysis on all cells after gridding, and then filter for my cell types of interest only during the visualization/plotting stage?

              I would greatly appreciate your guidance on the recommended best practice for this scenario.
              Thank you in advance for your time and help!

              Metadata

              Metadata

              Assignees

              No one assigned

                Labels

                No labels
                No labels

                Projects

                No projects

                  Milestone

                  No milestone

                  Relationships

                  None yet

                  Development

                  No branches or pull requests

                  Issue actions

                  , '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" + ' Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types · Issue #347 · BiomedicalMachineLearning/stLearn · GitHub
                  Skip to content

                  Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types #347

                  Description

                  @meisproject

                  Thank you for developing such an excellent software tool!

                  I'm currently using stlearn for CCI analysis on Visium HD data, and I'm only interested in a subset of specific cell types.

                  I have a question about the optimal workflow: Should I filter the AnnData object to retain only my cell types of interest before performing CCI analysis (note that these cell types may not be spatially contiguous), or should I follow the Xenium tutorial approach by performing CCI analysis on all cells after gridding, and then filter for my cell types of interest only during the visualization/plotting stage?

                  I would greatly appreciate your guidance on the recommended best practice for this scenario.
                  Thank you in advance for your time and help!

                  Metadata

                  Metadata

                  Assignees

                  No one assigned

                    Labels

                    No labels
                    No labels

                    Projects

                    No projects

                      Milestone

                      No milestone

                      Relationships

                      None yet

                      Development

                      No branches or pull requests

                      Issue actions

                      , '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('^' + ".*" + ' Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types · Issue #347 · BiomedicalMachineLearning/stLearn · GitHub
                      Skip to content

                      Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types #347

                      Description

                      @meisproject

                      Thank you for developing such an excellent software tool!

                      I'm currently using stlearn for CCI analysis on Visium HD data, and I'm only interested in a subset of specific cell types.

                      I have a question about the optimal workflow: Should I filter the AnnData object to retain only my cell types of interest before performing CCI analysis (note that these cell types may not be spatially contiguous), or should I follow the Xenium tutorial approach by performing CCI analysis on all cells after gridding, and then filter for my cell types of interest only during the visualization/plotting stage?

                      I would greatly appreciate your guidance on the recommended best practice for this scenario.
                      Thank you in advance for your time and help!

                      Metadata

                      Metadata

                      Assignees

                      No one assigned

                        Labels

                        No labels
                        No labels

                        Projects

                        No projects

                          Milestone

                          No milestone

                          Relationships

                          None yet

                          Development

                          No branches or pull requests

                          Issue actions

                          , '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('^' + ".*" + ' Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types · Issue #347 · BiomedicalMachineLearning/stLearn · GitHub
                          Skip to content

                          Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types #347

                          Description

                          @meisproject

                          Thank you for developing such an excellent software tool!

                          I'm currently using stlearn for CCI analysis on Visium HD data, and I'm only interested in a subset of specific cell types.

                          I have a question about the optimal workflow: Should I filter the AnnData object to retain only my cell types of interest before performing CCI analysis (note that these cell types may not be spatially contiguous), or should I follow the Xenium tutorial approach by performing CCI analysis on all cells after gridding, and then filter for my cell types of interest only during the visualization/plotting stage?

                          I would greatly appreciate your guidance on the recommended best practice for this scenario.
                          Thank you in advance for your time and help!

                          Metadata

                          Metadata

                          Assignees

                          No one assigned

                            Labels

                            No labels
                            No labels

                            Projects

                            No projects

                              Milestone

                              No milestone

                              Relationships

                              None yet

                              Development

                              No branches or pull requests

                              Issue actions

                              , '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); } })(); })(); Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types · Issue #347 · BiomedicalMachineLearning/stLearn · GitHub
                              Skip to content

                              Best Practice for CCI Analysis on Visium HD Data with Subset Cell Types #347

                              Description

                              @meisproject

                              Thank you for developing such an excellent software tool!

                              I'm currently using stlearn for CCI analysis on Visium HD data, and I'm only interested in a subset of specific cell types.

                              I have a question about the optimal workflow: Should I filter the AnnData object to retain only my cell types of interest before performing CCI analysis (note that these cell types may not be spatially contiguous), or should I follow the Xenium tutorial approach by performing CCI analysis on all cells after gridding, and then filter for my cell types of interest only during the visualization/plotting stage?

                              I would greatly appreciate your guidance on the recommended best practice for this scenario.
                              Thank you in advance for your time and help!

                              Metadata

                              Metadata

                              Assignees

                              No one assigned

                                Labels

                                No labels
                                No labels

                                Projects

                                No projects

                                  Milestone

                                  No milestone

                                  Relationships

                                  None yet

                                  Development

                                  No branches or pull requests

                                  Issue actions