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Distributed ComBat (dComBat)

Implementation of ComBat for a distributed data setting

Maintained by Andrew Chen, andrewac@pennmedicine.upenn.edu

1. Background

ComBat is a widely-used harmonization method that has proven to be effective in both genomic and neuroimaging contexts. For clinical data housed in separate locations however, the ComBat method could not be applied. We adapt ComBat for this distributed data setting as distributed ComBat (dComBat) and provide an implementation based on the current R package for ComBat (https://github.com/Jfortin1/ComBatHarmonization).

If you use this method please cite the following paper:

Chen, A. A., Luo, C., Chen, Y., Shinohara, R. T., & Shou, H. (2022). Privacy-preserving harmonization via distributed ComBat. NeuroImage, 248, 118822. https://doi.org/10.1016/j.neuroimage.2021.118822

2. Usage

This code is meant to be used without installation to avoid potential complications coordinating across separate data locations. The only current dependency is the package matrixStats, but this may be changed in future versions. neuroCombat_helpers.R and neuroCombat.R are sourced directly from the R implementation of ComBat (https://github.com/Jfortin1/ComBatHarmonization).

Two sample codes are provided dCombat_central_sample.R and dCombat_site_sample.R. The best way to use our code as follows:

  1. Send dCombat_site_sample.R to each site and have individual data coordinators adapt that code for their data. The site script will output deidentified summary statistics that can then be sent to a central location.
  2. Share the summary statistics with a central location, which modifies dCombat_central_sample.R to include these files. This will produce another file, which needs to be sent back to the sites for a second step.
  3. After sharing the central location output file, have data coordinators adapt the dCombat_site_sample.R code to run a second step, which outputs another set of summary statistics for the last step.
  4. Share the second set of summary statistics with the central location, which updates dCombat_central_sample.R to output a final set of harmonization parameters.
  5. Send these harmonization parameters to each site, which can then perform the final dComBat locally.

3. In other programming languages

The Python version of this code is available in the Python/ directory.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
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}
} 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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Distributed ComBat (dComBat)

Implementation of ComBat for a distributed data setting

Maintained by Andrew Chen, andrewac@pennmedicine.upenn.edu

1. Background

ComBat is a widely-used harmonization method that has proven to be effective in both genomic and neuroimaging contexts. For clinical data housed in separate locations however, the ComBat method could not be applied. We adapt ComBat for this distributed data setting as distributed ComBat (dComBat) and provide an implementation based on the current R package for ComBat (https://github.com/Jfortin1/ComBatHarmonization).

If you use this method please cite the following paper:

Chen, A. A., Luo, C., Chen, Y., Shinohara, R. T., & Shou, H. (2022). Privacy-preserving harmonization via distributed ComBat. NeuroImage, 248, 118822. https://doi.org/10.1016/j.neuroimage.2021.118822

2. Usage

This code is meant to be used without installation to avoid potential complications coordinating across separate data locations. The only current dependency is the package matrixStats, but this may be changed in future versions. neuroCombat_helpers.R and neuroCombat.R are sourced directly from the R implementation of ComBat (https://github.com/Jfortin1/ComBatHarmonization).

Two sample codes are provided dCombat_central_sample.R and dCombat_site_sample.R. The best way to use our code as follows:

  1. Send dCombat_site_sample.R to each site and have individual data coordinators adapt that code for their data. The site script will output deidentified summary statistics that can then be sent to a central location.
  2. Share the summary statistics with a central location, which modifies dCombat_central_sample.R to include these files. This will produce another file, which needs to be sent back to the sites for a second step.
  3. After sharing the central location output file, have data coordinators adapt the dCombat_site_sample.R code to run a second step, which outputs another set of summary statistics for the last step.
  4. Share the second set of summary statistics with the central location, which updates dCombat_central_sample.R to output a final set of harmonization parameters.
  5. Send these harmonization parameters to each site, which can then perform the final dComBat locally.

3. In other programming languages

The Python version of this code is available in the Python/ directory.

About

Adaptation of ComBat for a distributed data setting

Resources

Stars

1 star

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1 watching

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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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Distributed ComBat (dComBat)

Implementation of ComBat for a distributed data setting

Maintained by Andrew Chen, andrewac@pennmedicine.upenn.edu

1. Background

ComBat is a widely-used harmonization method that has proven to be effective in both genomic and neuroimaging contexts. For clinical data housed in separate locations however, the ComBat method could not be applied. We adapt ComBat for this distributed data setting as distributed ComBat (dComBat) and provide an implementation based on the current R package for ComBat (https://github.com/Jfortin1/ComBatHarmonization).

If you use this method please cite the following paper:

Chen, A. A., Luo, C., Chen, Y., Shinohara, R. T., & Shou, H. (2022). Privacy-preserving harmonization via distributed ComBat. NeuroImage, 248, 118822. https://doi.org/10.1016/j.neuroimage.2021.118822

2. Usage

This code is meant to be used without installation to avoid potential complications coordinating across separate data locations. The only current dependency is the package matrixStats, but this may be changed in future versions. neuroCombat_helpers.R and neuroCombat.R are sourced directly from the R implementation of ComBat (https://github.com/Jfortin1/ComBatHarmonization).

Two sample codes are provided dCombat_central_sample.R and dCombat_site_sample.R. The best way to use our code as follows:

  1. Send dCombat_site_sample.R to each site and have individual data coordinators adapt that code for their data. The site script will output deidentified summary statistics that can then be sent to a central location.
  2. Share the summary statistics with a central location, which modifies dCombat_central_sample.R to include these files. This will produce another file, which needs to be sent back to the sites for a second step.
  3. After sharing the central location output file, have data coordinators adapt the dCombat_site_sample.R code to run a second step, which outputs another set of summary statistics for the last step.
  4. Share the second set of summary statistics with the central location, which updates dCombat_central_sample.R to output a final set of harmonization parameters.
  5. Send these harmonization parameters to each site, which can then perform the final dComBat locally.

3. In other programming languages

The Python version of this code is available in the Python/ directory.

About

Adaptation of ComBat for a distributed data setting

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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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Distributed ComBat (dComBat)

Implementation of ComBat for a distributed data setting

Maintained by Andrew Chen, andrewac@pennmedicine.upenn.edu

1. Background

ComBat is a widely-used harmonization method that has proven to be effective in both genomic and neuroimaging contexts. For clinical data housed in separate locations however, the ComBat method could not be applied. We adapt ComBat for this distributed data setting as distributed ComBat (dComBat) and provide an implementation based on the current R package for ComBat (https://github.com/Jfortin1/ComBatHarmonization).

If you use this method please cite the following paper:

Chen, A. A., Luo, C., Chen, Y., Shinohara, R. T., & Shou, H. (2022). Privacy-preserving harmonization via distributed ComBat. NeuroImage, 248, 118822. https://doi.org/10.1016/j.neuroimage.2021.118822

2. Usage

This code is meant to be used without installation to avoid potential complications coordinating across separate data locations. The only current dependency is the package matrixStats, but this may be changed in future versions. neuroCombat_helpers.R and neuroCombat.R are sourced directly from the R implementation of ComBat (https://github.com/Jfortin1/ComBatHarmonization).

Two sample codes are provided dCombat_central_sample.R and dCombat_site_sample.R. The best way to use our code as follows:

  1. Send dCombat_site_sample.R to each site and have individual data coordinators adapt that code for their data. The site script will output deidentified summary statistics that can then be sent to a central location.
  2. Share the summary statistics with a central location, which modifies dCombat_central_sample.R to include these files. This will produce another file, which needs to be sent back to the sites for a second step.
  3. After sharing the central location output file, have data coordinators adapt the dCombat_site_sample.R code to run a second step, which outputs another set of summary statistics for the last step.
  4. Share the second set of summary statistics with the central location, which updates dCombat_central_sample.R to output a final set of harmonization parameters.
  5. Send these harmonization parameters to each site, which can then perform the final dComBat locally.

3. In other programming languages

The Python version of this code is available in the Python/ directory.

About

Adaptation of ComBat for a distributed data setting

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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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Distributed ComBat (dComBat)

Implementation of ComBat for a distributed data setting

Maintained by Andrew Chen, andrewac@pennmedicine.upenn.edu

1. Background

ComBat is a widely-used harmonization method that has proven to be effective in both genomic and neuroimaging contexts. For clinical data housed in separate locations however, the ComBat method could not be applied. We adapt ComBat for this distributed data setting as distributed ComBat (dComBat) and provide an implementation based on the current R package for ComBat (https://github.com/Jfortin1/ComBatHarmonization).

If you use this method please cite the following paper:

Chen, A. A., Luo, C., Chen, Y., Shinohara, R. T., & Shou, H. (2022). Privacy-preserving harmonization via distributed ComBat. NeuroImage, 248, 118822. https://doi.org/10.1016/j.neuroimage.2021.118822

2. Usage

This code is meant to be used without installation to avoid potential complications coordinating across separate data locations. The only current dependency is the package matrixStats, but this may be changed in future versions. neuroCombat_helpers.R and neuroCombat.R are sourced directly from the R implementation of ComBat (https://github.com/Jfortin1/ComBatHarmonization).

Two sample codes are provided dCombat_central_sample.R and dCombat_site_sample.R. The best way to use our code as follows:

  1. Send dCombat_site_sample.R to each site and have individual data coordinators adapt that code for their data. The site script will output deidentified summary statistics that can then be sent to a central location.
  2. Share the summary statistics with a central location, which modifies dCombat_central_sample.R to include these files. This will produce another file, which needs to be sent back to the sites for a second step.
  3. After sharing the central location output file, have data coordinators adapt the dCombat_site_sample.R code to run a second step, which outputs another set of summary statistics for the last step.
  4. Share the second set of summary statistics with the central location, which updates dCombat_central_sample.R to output a final set of harmonization parameters.
  5. Send these harmonization parameters to each site, which can then perform the final dComBat locally.

3. In other programming languages

The Python version of this code is available in the Python/ directory.

About

Adaptation of ComBat for a distributed data setting

Resources

Stars

1 star

Watchers

1 watching

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Languages

, '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); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Distributed ComBat (dComBat)

Implementation of ComBat for a distributed data setting

Maintained by Andrew Chen, andrewac@pennmedicine.upenn.edu

1. Background

ComBat is a widely-used harmonization method that has proven to be effective in both genomic and neuroimaging contexts. For clinical data housed in separate locations however, the ComBat method could not be applied. We adapt ComBat for this distributed data setting as distributed ComBat (dComBat) and provide an implementation based on the current R package for ComBat (https://github.com/Jfortin1/ComBatHarmonization).

If you use this method please cite the following paper:

Chen, A. A., Luo, C., Chen, Y., Shinohara, R. T., & Shou, H. (2022). Privacy-preserving harmonization via distributed ComBat. NeuroImage, 248, 118822. https://doi.org/10.1016/j.neuroimage.2021.118822

2. Usage

This code is meant to be used without installation to avoid potential complications coordinating across separate data locations. The only current dependency is the package matrixStats, but this may be changed in future versions. neuroCombat_helpers.R and neuroCombat.R are sourced directly from the R implementation of ComBat (https://github.com/Jfortin1/ComBatHarmonization).

Two sample codes are provided dCombat_central_sample.R and dCombat_site_sample.R. The best way to use our code as follows:

  1. Send dCombat_site_sample.R to each site and have individual data coordinators adapt that code for their data. The site script will output deidentified summary statistics that can then be sent to a central location.
  2. Share the summary statistics with a central location, which modifies dCombat_central_sample.R to include these files. This will produce another file, which needs to be sent back to the sites for a second step.
  3. After sharing the central location output file, have data coordinators adapt the dCombat_site_sample.R code to run a second step, which outputs another set of summary statistics for the last step.
  4. Share the second set of summary statistics with the central location, which updates dCombat_central_sample.R to output a final set of harmonization parameters.
  5. Send these harmonization parameters to each site, which can then perform the final dComBat locally.

3. In other programming languages

The Python version of this code is available in the Python/ directory.

About

Adaptation of ComBat for a distributed data setting

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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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Distributed ComBat (dComBat)

Implementation of ComBat for a distributed data setting

Maintained by Andrew Chen, andrewac@pennmedicine.upenn.edu

1. Background

ComBat is a widely-used harmonization method that has proven to be effective in both genomic and neuroimaging contexts. For clinical data housed in separate locations however, the ComBat method could not be applied. We adapt ComBat for this distributed data setting as distributed ComBat (dComBat) and provide an implementation based on the current R package for ComBat (https://github.com/Jfortin1/ComBatHarmonization).

If you use this method please cite the following paper:

Chen, A. A., Luo, C., Chen, Y., Shinohara, R. T., & Shou, H. (2022). Privacy-preserving harmonization via distributed ComBat. NeuroImage, 248, 118822. https://doi.org/10.1016/j.neuroimage.2021.118822

2. Usage

This code is meant to be used without installation to avoid potential complications coordinating across separate data locations. The only current dependency is the package matrixStats, but this may be changed in future versions. neuroCombat_helpers.R and neuroCombat.R are sourced directly from the R implementation of ComBat (https://github.com/Jfortin1/ComBatHarmonization).

Two sample codes are provided dCombat_central_sample.R and dCombat_site_sample.R. The best way to use our code as follows:

  1. Send dCombat_site_sample.R to each site and have individual data coordinators adapt that code for their data. The site script will output deidentified summary statistics that can then be sent to a central location.
  2. Share the summary statistics with a central location, which modifies dCombat_central_sample.R to include these files. This will produce another file, which needs to be sent back to the sites for a second step.
  3. After sharing the central location output file, have data coordinators adapt the dCombat_site_sample.R code to run a second step, which outputs another set of summary statistics for the last step.
  4. Share the second set of summary statistics with the central location, which updates dCombat_central_sample.R to output a final set of harmonization parameters.
  5. Send these harmonization parameters to each site, which can then perform the final dComBat locally.

3. In other programming languages

The Python version of this code is available in the Python/ directory.

About

Adaptation of ComBat for a distributed data setting

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, 'i'); 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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Distributed ComBat (dComBat)

Implementation of ComBat for a distributed data setting

Maintained by Andrew Chen, andrewac@pennmedicine.upenn.edu

1. Background

ComBat is a widely-used harmonization method that has proven to be effective in both genomic and neuroimaging contexts. For clinical data housed in separate locations however, the ComBat method could not be applied. We adapt ComBat for this distributed data setting as distributed ComBat (dComBat) and provide an implementation based on the current R package for ComBat (https://github.com/Jfortin1/ComBatHarmonization).

If you use this method please cite the following paper:

Chen, A. A., Luo, C., Chen, Y., Shinohara, R. T., & Shou, H. (2022). Privacy-preserving harmonization via distributed ComBat. NeuroImage, 248, 118822. https://doi.org/10.1016/j.neuroimage.2021.118822

2. Usage

This code is meant to be used without installation to avoid potential complications coordinating across separate data locations. The only current dependency is the package matrixStats, but this may be changed in future versions. neuroCombat_helpers.R and neuroCombat.R are sourced directly from the R implementation of ComBat (https://github.com/Jfortin1/ComBatHarmonization).

Two sample codes are provided dCombat_central_sample.R and dCombat_site_sample.R. The best way to use our code as follows:

  1. Send dCombat_site_sample.R to each site and have individual data coordinators adapt that code for their data. The site script will output deidentified summary statistics that can then be sent to a central location.
  2. Share the summary statistics with a central location, which modifies dCombat_central_sample.R to include these files. This will produce another file, which needs to be sent back to the sites for a second step.
  3. After sharing the central location output file, have data coordinators adapt the dCombat_site_sample.R code to run a second step, which outputs another set of summary statistics for the last step.
  4. Share the second set of summary statistics with the central location, which updates dCombat_central_sample.R to output a final set of harmonization parameters.
  5. Send these harmonization parameters to each site, which can then perform the final dComBat locally.

3. In other programming languages

The Python version of this code is available in the Python/ directory.

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