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diffcyt

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Introduction

diffcyt: R package for differential discovery in high-dimensional cytometry via high-resolution clustering

The diffcyt package implements statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.

Details and citation

For details on the statistical methodology and comparisons with existing approaches, see our paper:

Tutorial and examples

For a tutorial and examples of usage, see the Bioconductor package vignette (link also available via the main Bioconductor page for the diffcyt package).

Installation

The diffcyt package is available from Bioconductor, and can be installed as follows:

# Install Bioconductor installer from CRAN
install.packages("BiocManager")
# Install 'diffcyt' package from Bioconductor
BiocManager::install("diffcyt")

To run the examples in the package vignette and generate additional visualizations, the HDCytoData and CATALYST packages from Bioconductor are also required.

BiocManager::install(c("HDCytoData", "CATALYST"))

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R package for differential discovery analyses in high-dimensional cytometry data

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

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diffcyt
R build status
Introduction
diffcyt: R package for differential discovery in high-dimensional cytometry via high-resolution clustering
The diffcyt package implements statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.
Details and citation
For details on the statistical methodology and comparisons with existing approaches, see our paper:
Tutorial and examples
For a tutorial and examples of usage, see the Bioconductor package vignette (link also available via the main Bioconductor page for the diffcyt package).
Installation
The diffcyt package is available from Bioconductor, and can be installed as follows:
# Install Bioconductor installer from CRAN
install.packages("BiocManager")
# Install 'diffcyt' package from Bioconductor
BiocManager::install("diffcyt")

To run the examples in the package vignette and generate additional visualizations, the HDCytoData and CATALYST packages from Bioconductor are also required.
BiocManager::install(c("HDCytoData", "CATALYST"))

About
R package for differential discovery analyses in high-dimensional cytometry data
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MIT license
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5 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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diffcyt
R build status
Introduction
diffcyt: R package for differential discovery in high-dimensional cytometry via high-resolution clustering
The diffcyt package implements statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.
Details and citation
For details on the statistical methodology and comparisons with existing approaches, see our paper:
Tutorial and examples
For a tutorial and examples of usage, see the Bioconductor package vignette (link also available via the main Bioconductor page for the diffcyt package).
Installation
The diffcyt package is available from Bioconductor, and can be installed as follows:
# Install Bioconductor installer from CRAN
install.packages("BiocManager")
# Install 'diffcyt' package from Bioconductor
BiocManager::install("diffcyt")

To run the examples in the package vignette and generate additional visualizations, the HDCytoData and CATALYST packages from Bioconductor are also required.
BiocManager::install(c("HDCytoData", "CATALYST"))

About
R package for differential discovery analyses in high-dimensional cytometry data
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Readme
MIT license
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Stars
25 stars
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5 watching
Forks
12 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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diffcyt
R build status
Introduction
diffcyt: R package for differential discovery in high-dimensional cytometry via high-resolution clustering
The diffcyt package implements statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.
Details and citation
For details on the statistical methodology and comparisons with existing approaches, see our paper:
Tutorial and examples
For a tutorial and examples of usage, see the Bioconductor package vignette (link also available via the main Bioconductor page for the diffcyt package).
Installation
The diffcyt package is available from Bioconductor, and can be installed as follows:
# Install Bioconductor installer from CRAN
install.packages("BiocManager")
# Install 'diffcyt' package from Bioconductor
BiocManager::install("diffcyt")

To run the examples in the package vignette and generate additional visualizations, the HDCytoData and CATALYST packages from Bioconductor are also required.
BiocManager::install(c("HDCytoData", "CATALYST"))

About
R package for differential discovery analyses in high-dimensional cytometry data
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Readme
MIT license
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5 watching
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12 forks
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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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diffcyt
R build status
Introduction
diffcyt: R package for differential discovery in high-dimensional cytometry via high-resolution clustering
The diffcyt package implements statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.
Details and citation
For details on the statistical methodology and comparisons with existing approaches, see our paper:
Tutorial and examples
For a tutorial and examples of usage, see the Bioconductor package vignette (link also available via the main Bioconductor page for the diffcyt package).
Installation
The diffcyt package is available from Bioconductor, and can be installed as follows:
# Install Bioconductor installer from CRAN
install.packages("BiocManager")
# Install 'diffcyt' package from Bioconductor
BiocManager::install("diffcyt")

To run the examples in the package vignette and generate additional visualizations, the HDCytoData and CATALYST packages from Bioconductor are also required.
BiocManager::install(c("HDCytoData", "CATALYST"))

About
R package for differential discovery analyses in high-dimensional cytometry data
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Readme
MIT license
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Stars
25 stars
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5 watching
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12 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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diffcyt
R build status
Introduction
diffcyt: R package for differential discovery in high-dimensional cytometry via high-resolution clustering
The diffcyt package implements statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.
Details and citation
For details on the statistical methodology and comparisons with existing approaches, see our paper:
Tutorial and examples
For a tutorial and examples of usage, see the Bioconductor package vignette (link also available via the main Bioconductor page for the diffcyt package).
Installation
The diffcyt package is available from Bioconductor, and can be installed as follows:
# Install Bioconductor installer from CRAN
install.packages("BiocManager")
# Install 'diffcyt' package from Bioconductor
BiocManager::install("diffcyt")

To run the examples in the package vignette and generate additional visualizations, the HDCytoData and CATALYST packages from Bioconductor are also required.
BiocManager::install(c("HDCytoData", "CATALYST"))

About
R package for differential discovery analyses in high-dimensional cytometry data
Resources
Readme
MIT license
Activity
Stars
25 stars
Watchers
5 watching
Forks
12 forks
Report repository
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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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diffcyt
R build status
Introduction
diffcyt: R package for differential discovery in high-dimensional cytometry via high-resolution clustering
The diffcyt package implements statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.
Details and citation
For details on the statistical methodology and comparisons with existing approaches, see our paper:
Tutorial and examples
For a tutorial and examples of usage, see the Bioconductor package vignette (link also available via the main Bioconductor page for the diffcyt package).
Installation
The diffcyt package is available from Bioconductor, and can be installed as follows:
# Install Bioconductor installer from CRAN
install.packages("BiocManager")
# Install 'diffcyt' package from Bioconductor
BiocManager::install("diffcyt")

To run the examples in the package vignette and generate additional visualizations, the HDCytoData and CATALYST packages from Bioconductor are also required.
BiocManager::install(c("HDCytoData", "CATALYST"))

About
R package for differential discovery analyses in high-dimensional cytometry data
Resources
Readme
MIT license
Activity
Stars
25 stars
Watchers
5 watching
Forks
12 forks
Report repository
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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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diffcyt
R build status
Introduction
diffcyt: R package for differential discovery in high-dimensional cytometry via high-resolution clustering
The diffcyt package implements statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.
Details and citation
For details on the statistical methodology and comparisons with existing approaches, see our paper:
Tutorial and examples
For a tutorial and examples of usage, see the Bioconductor package vignette (link also available via the main Bioconductor page for the diffcyt package).
Installation
The diffcyt package is available from Bioconductor, and can be installed as follows:
# Install Bioconductor installer from CRAN
install.packages("BiocManager")
# Install 'diffcyt' package from Bioconductor
BiocManager::install("diffcyt")

To run the examples in the package vignette and generate additional visualizations, the HDCytoData and CATALYST packages from Bioconductor are also required.
BiocManager::install(c("HDCytoData", "CATALYST"))

About
R package for differential discovery analyses in high-dimensional cytometry data
Resources
Readme
MIT license
Activity
Stars
25 stars
Watchers
5 watching
Forks
12 forks
Report repository
Releases
Packages
Used by
Contributors
Languages