BranchesTags

Latest commit

History

14 Commits

Folders and files

NameName
Last commit message
Last commit date
code
code
data
data
LICENSE
LICENSE
README.md
README.md

Repository files navigation

Disruption Networks

This is a public repository for the code associated to the paper: Gerassy-Vainberg et al. A personalized network framework reveals predictive axis of anti-TNF response across diseases. The analyses done in this study were divided in several reports as follows:

NotebookInput dataFigures
Data pre-processing- The microarray raw data generated in this study: GSE186963.
- The CyTOF raw data generated in this study: FlowRepository FR-FCM-Z4MQ.
- Luminex data – in GitHub data directory.
-Additional files for the analysis in the data directory		S2
Dynamics_and_baseline_analyses_DNets- GSE94648
-Additional input files for the analysis in the data directory		Fig 1, 2, 3, 4a-b
S1, S3, S4, S5
scRNAseq_analysis_of_the_response_disrupted_pathways- The scRNA-seq data generated in this study: PRJNA779701.
-Additional input files for the analysis in the data directory		Fig 4c-d,
S6
Predictive_signature_and_validation- GSE20690
- GSE33377
- GSE42296
- in-house CD cohort- qPCR results- in GitHub data directory
-scRNAseq processed data
- CyTOF processed data
- Additional input files for the analysis in the data directory		Fig 4e, Fig 5
S7, S8
Disruption_Networks_functions

Whether non-responders' transcriptional profile reflects fundamental routes of IFX resistance, is essential for tailoring treatment. To elucidate molecular mechanisms of individual-specific pathways of treatment non-response, we devised a systematic framework we term ‘Disruption Networks’ which generates a new data-type to provide individual-level information of cell-centered changes in cross-feature relations. The generation of the new data-type relies on studying relations between features across a predefined reference population of individuals (i.e., a population level reference network), and then inferring how these relations differ (i.e., are disrupted) at the single sample level. The new data-type can serve as an input to multiple analyses including integration, differential signal detection, patient stratification based on disruption profile, assessment of disruption in functional modules and evaluation of individual’s molecular network behavior under specific perturbation effects or biological conditions

image

About

No description, website, or topics provided.

Resources

Readme
License
Activity
Custom properties

Stars

2 stars

Watchers

0 watching

Forks

1 fork
Report repository

Releases

Packages

Contributors

Languages

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

Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
code
code
data
data
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Disruption Networks
This is a public repository for the code associated to the paper: Gerassy-Vainberg et al. A personalized network framework reveals predictive axis of anti-TNF response across diseases.
The analyses done in this study were divided in several reports as follows:
NotebookInput dataFigures
Data pre-processing- The microarray raw data generated in this study: GSE186963. 
 - The CyTOF raw data generated in this study: FlowRepository FR-FCM-Z4MQ. 
 - Luminex data – in GitHub data directory. 
 -Additional files for the analysis in the data directory		S2
Dynamics_and_baseline_analyses_DNets- GSE94648 
 -Additional input files for the analysis in the data directory		Fig 1, 2, 3, 4a-b 
 S1, S3, S4, S5
scRNAseq_analysis_of_the_response_disrupted_pathways- The scRNA-seq data generated in this study: PRJNA779701. 
 -Additional input files for the analysis in the data directory		Fig 4c-d, 
 S6
Predictive_signature_and_validation- GSE20690 
 - GSE33377 
 - GSE42296 
 - in-house CD cohort- qPCR results- in GitHub data directory 
 -scRNAseq processed data 
 - CyTOF processed data 
 - Additional input files for the analysis in the data directory		Fig 4e, Fig 5 
 S7, S8
Disruption_Networks_functions

 Whether non-responders' transcriptional profile reflects fundamental routes of IFX resistance, is essential for tailoring treatment. To elucidate molecular mechanisms of individual-specific pathways of treatment non-response, we devised a systematic framework we term ‘Disruption Networks’ which generates a new data-type to provide individual-level information of cell-centered changes in cross-feature relations. The generation of the new data-type relies on studying relations between features across a predefined reference population of individuals (i.e., a population level reference network), and then inferring how these relations differ (i.e., are disrupted) at the single sample level. The new data-type can serve as an input to multiple analyses including integration, differential signal detection, patient stratification based on disruption profile, assessment of disruption in functional modules and evaluation of individual’s molecular network behavior under specific perturbation effects or biological conditions

image
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
code
code
data
data
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Disruption Networks
This is a public repository for the code associated to the paper: Gerassy-Vainberg et al. A personalized network framework reveals predictive axis of anti-TNF response across diseases.
The analyses done in this study were divided in several reports as follows:
NotebookInput dataFigures
Data pre-processing- The microarray raw data generated in this study: GSE186963. 
 - The CyTOF raw data generated in this study: FlowRepository FR-FCM-Z4MQ. 
 - Luminex data – in GitHub data directory. 
 -Additional files for the analysis in the data directory		S2
Dynamics_and_baseline_analyses_DNets- GSE94648 
 -Additional input files for the analysis in the data directory		Fig 1, 2, 3, 4a-b 
 S1, S3, S4, S5
scRNAseq_analysis_of_the_response_disrupted_pathways- The scRNA-seq data generated in this study: PRJNA779701. 
 -Additional input files for the analysis in the data directory		Fig 4c-d, 
 S6
Predictive_signature_and_validation- GSE20690 
 - GSE33377 
 - GSE42296 
 - in-house CD cohort- qPCR results- in GitHub data directory 
 -scRNAseq processed data 
 - CyTOF processed data 
 - Additional input files for the analysis in the data directory		Fig 4e, Fig 5 
 S7, S8
Disruption_Networks_functions

 Whether non-responders' transcriptional profile reflects fundamental routes of IFX resistance, is essential for tailoring treatment. To elucidate molecular mechanisms of individual-specific pathways of treatment non-response, we devised a systematic framework we term ‘Disruption Networks’ which generates a new data-type to provide individual-level information of cell-centered changes in cross-feature relations. The generation of the new data-type relies on studying relations between features across a predefined reference population of individuals (i.e., a population level reference network), and then inferring how these relations differ (i.e., are disrupted) at the single sample level. The new data-type can serve as an input to multiple analyses including integration, differential signal detection, patient stratification based on disruption profile, assessment of disruption in functional modules and evaluation of individual’s molecular network behavior under specific perturbation effects or biological conditions

image
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
code
code
data
data
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Disruption Networks
This is a public repository for the code associated to the paper: Gerassy-Vainberg et al. A personalized network framework reveals predictive axis of anti-TNF response across diseases.
The analyses done in this study were divided in several reports as follows:
NotebookInput dataFigures
Data pre-processing- The microarray raw data generated in this study: GSE186963. 
 - The CyTOF raw data generated in this study: FlowRepository FR-FCM-Z4MQ. 
 - Luminex data – in GitHub data directory. 
 -Additional files for the analysis in the data directory		S2
Dynamics_and_baseline_analyses_DNets- GSE94648 
 -Additional input files for the analysis in the data directory		Fig 1, 2, 3, 4a-b 
 S1, S3, S4, S5
scRNAseq_analysis_of_the_response_disrupted_pathways- The scRNA-seq data generated in this study: PRJNA779701. 
 -Additional input files for the analysis in the data directory		Fig 4c-d, 
 S6
Predictive_signature_and_validation- GSE20690 
 - GSE33377 
 - GSE42296 
 - in-house CD cohort- qPCR results- in GitHub data directory 
 -scRNAseq processed data 
 - CyTOF processed data 
 - Additional input files for the analysis in the data directory		Fig 4e, Fig 5 
 S7, S8
Disruption_Networks_functions

 Whether non-responders' transcriptional profile reflects fundamental routes of IFX resistance, is essential for tailoring treatment. To elucidate molecular mechanisms of individual-specific pathways of treatment non-response, we devised a systematic framework we term ‘Disruption Networks’ which generates a new data-type to provide individual-level information of cell-centered changes in cross-feature relations. The generation of the new data-type relies on studying relations between features across a predefined reference population of individuals (i.e., a population level reference network), and then inferring how these relations differ (i.e., are disrupted) at the single sample level. The new data-type can serve as an input to multiple analyses including integration, differential signal detection, patient stratification based on disruption profile, assessment of disruption in functional modules and evaluation of individual’s molecular network behavior under specific perturbation effects or biological conditions

image
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, '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" + '

Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
code
code
data
data
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Disruption Networks
This is a public repository for the code associated to the paper: Gerassy-Vainberg et al. A personalized network framework reveals predictive axis of anti-TNF response across diseases.
The analyses done in this study were divided in several reports as follows:
NotebookInput dataFigures
Data pre-processing- The microarray raw data generated in this study: GSE186963. 
 - The CyTOF raw data generated in this study: FlowRepository FR-FCM-Z4MQ. 
 - Luminex data – in GitHub data directory. 
 -Additional files for the analysis in the data directory		S2
Dynamics_and_baseline_analyses_DNets- GSE94648 
 -Additional input files for the analysis in the data directory		Fig 1, 2, 3, 4a-b 
 S1, S3, S4, S5
scRNAseq_analysis_of_the_response_disrupted_pathways- The scRNA-seq data generated in this study: PRJNA779701. 
 -Additional input files for the analysis in the data directory		Fig 4c-d, 
 S6
Predictive_signature_and_validation- GSE20690 
 - GSE33377 
 - GSE42296 
 - in-house CD cohort- qPCR results- in GitHub data directory 
 -scRNAseq processed data 
 - CyTOF processed data 
 - Additional input files for the analysis in the data directory		Fig 4e, Fig 5 
 S7, S8
Disruption_Networks_functions

 Whether non-responders' transcriptional profile reflects fundamental routes of IFX resistance, is essential for tailoring treatment. To elucidate molecular mechanisms of individual-specific pathways of treatment non-response, we devised a systematic framework we term ‘Disruption Networks’ which generates a new data-type to provide individual-level information of cell-centered changes in cross-feature relations. The generation of the new data-type relies on studying relations between features across a predefined reference population of individuals (i.e., a population level reference network), and then inferring how these relations differ (i.e., are disrupted) at the single sample level. The new data-type can serve as an input to multiple analyses including integration, differential signal detection, patient stratification based on disruption profile, assessment of disruption in functional modules and evaluation of individual’s molecular network behavior under specific perturbation effects or biological conditions

image
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
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('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
code
code
data
data
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Disruption Networks
This is a public repository for the code associated to the paper: Gerassy-Vainberg et al. A personalized network framework reveals predictive axis of anti-TNF response across diseases.
The analyses done in this study were divided in several reports as follows:
NotebookInput dataFigures
Data pre-processing- The microarray raw data generated in this study: GSE186963. 
 - The CyTOF raw data generated in this study: FlowRepository FR-FCM-Z4MQ. 
 - Luminex data – in GitHub data directory. 
 -Additional files for the analysis in the data directory		S2
Dynamics_and_baseline_analyses_DNets- GSE94648 
 -Additional input files for the analysis in the data directory		Fig 1, 2, 3, 4a-b 
 S1, S3, S4, S5
scRNAseq_analysis_of_the_response_disrupted_pathways- The scRNA-seq data generated in this study: PRJNA779701. 
 -Additional input files for the analysis in the data directory		Fig 4c-d, 
 S6
Predictive_signature_and_validation- GSE20690 
 - GSE33377 
 - GSE42296 
 - in-house CD cohort- qPCR results- in GitHub data directory 
 -scRNAseq processed data 
 - CyTOF processed data 
 - Additional input files for the analysis in the data directory		Fig 4e, Fig 5 
 S7, S8
Disruption_Networks_functions

 Whether non-responders' transcriptional profile reflects fundamental routes of IFX resistance, is essential for tailoring treatment. To elucidate molecular mechanisms of individual-specific pathways of treatment non-response, we devised a systematic framework we term ‘Disruption Networks’ which generates a new data-type to provide individual-level information of cell-centered changes in cross-feature relations. The generation of the new data-type relies on studying relations between features across a predefined reference population of individuals (i.e., a population level reference network), and then inferring how these relations differ (i.e., are disrupted) at the single sample level. The new data-type can serve as an input to multiple analyses including integration, differential signal detection, patient stratification based on disruption profile, assessment of disruption in functional modules and evaluation of individual’s molecular network behavior under specific perturbation effects or biological conditions

image
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
code
code
data
data
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Disruption Networks
This is a public repository for the code associated to the paper: Gerassy-Vainberg et al. A personalized network framework reveals predictive axis of anti-TNF response across diseases.
The analyses done in this study were divided in several reports as follows:
NotebookInput dataFigures
Data pre-processing- The microarray raw data generated in this study: GSE186963. 
 - The CyTOF raw data generated in this study: FlowRepository FR-FCM-Z4MQ. 
 - Luminex data – in GitHub data directory. 
 -Additional files for the analysis in the data directory		S2
Dynamics_and_baseline_analyses_DNets- GSE94648 
 -Additional input files for the analysis in the data directory		Fig 1, 2, 3, 4a-b 
 S1, S3, S4, S5
scRNAseq_analysis_of_the_response_disrupted_pathways- The scRNA-seq data generated in this study: PRJNA779701. 
 -Additional input files for the analysis in the data directory		Fig 4c-d, 
 S6
Predictive_signature_and_validation- GSE20690 
 - GSE33377 
 - GSE42296 
 - in-house CD cohort- qPCR results- in GitHub data directory 
 -scRNAseq processed data 
 - CyTOF processed data 
 - Additional input files for the analysis in the data directory		Fig 4e, Fig 5 
 S7, S8
Disruption_Networks_functions

 Whether non-responders' transcriptional profile reflects fundamental routes of IFX resistance, is essential for tailoring treatment. To elucidate molecular mechanisms of individual-specific pathways of treatment non-response, we devised a systematic framework we term ‘Disruption Networks’ which generates a new data-type to provide individual-level information of cell-centered changes in cross-feature relations. The generation of the new data-type relies on studying relations between features across a predefined reference population of individuals (i.e., a population level reference network), and then inferring how these relations differ (i.e., are disrupted) at the single sample level. The new data-type can serve as an input to multiple analyses including integration, differential signal detection, patient stratification based on disruption profile, assessment of disruption in functional modules and evaluation of individual’s molecular network behavior under specific perturbation effects or biological conditions

image
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, '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); }
})();
})();

Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
code
code
data
data
LICENSE
LICENSE
README.md
README.md
Repository files navigation
Disruption Networks
This is a public repository for the code associated to the paper: Gerassy-Vainberg et al. A personalized network framework reveals predictive axis of anti-TNF response across diseases.
The analyses done in this study were divided in several reports as follows:
NotebookInput dataFigures
Data pre-processing- The microarray raw data generated in this study: GSE186963. 
 - The CyTOF raw data generated in this study: FlowRepository FR-FCM-Z4MQ. 
 - Luminex data – in GitHub data directory. 
 -Additional files for the analysis in the data directory		S2
Dynamics_and_baseline_analyses_DNets- GSE94648 
 -Additional input files for the analysis in the data directory		Fig 1, 2, 3, 4a-b 
 S1, S3, S4, S5
scRNAseq_analysis_of_the_response_disrupted_pathways- The scRNA-seq data generated in this study: PRJNA779701. 
 -Additional input files for the analysis in the data directory		Fig 4c-d, 
 S6
Predictive_signature_and_validation- GSE20690 
 - GSE33377 
 - GSE42296 
 - in-house CD cohort- qPCR results- in GitHub data directory 
 -scRNAseq processed data 
 - CyTOF processed data 
 - Additional input files for the analysis in the data directory		Fig 4e, Fig 5 
 S7, S8
Disruption_Networks_functions

 Whether non-responders' transcriptional profile reflects fundamental routes of IFX resistance, is essential for tailoring treatment. To elucidate molecular mechanisms of individual-specific pathways of treatment non-response, we devised a systematic framework we term ‘Disruption Networks’ which generates a new data-type to provide individual-level information of cell-centered changes in cross-feature relations. The generation of the new data-type relies on studying relations between features across a predefined reference population of individuals (i.e., a population level reference network), and then inferring how these relations differ (i.e., are disrupted) at the single sample level. The new data-type can serve as an input to multiple analyses including integration, differential signal detection, patient stratification based on disruption profile, assessment of disruption in functional modules and evaluation of individual’s molecular network behavior under specific perturbation effects or biological conditions

image
About
No description, website, or topics provided.
Resources
Readme
License
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages