Skip to content
BranchesTags

Repository files navigation

DISSECT

DISSECT is a deep semi-supervised learning framework that performs complete deconvolution of gene expression mixtures such as bulk RNAseq, proteomics and spatial transcriptomics. The manuscript is available at Genome Biology.

This repository contains the functions to run DISSECT interactively in Python. The code to reproduce figures and run deconvolution pipeline through comamnd line is available here.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Folder tutorials include notebooks to get started.

Prerequisites

conda >= v22 through Anaconda or miniconda.

Installing

## Installation# Create and activate virtual environment. This is recommended to avoid conflict in dependencies.
conda create -y -n dissect python=3.9
conda activate dissect
# Clone DISSECT
git clone https://github.com/imsb-uke/DISSECT
# Install dependencies and DISSECT
pip install -r DISSECT/requirements.txt --use-deprecated=legacy-resolver
pip install DISSECT/.
# Install jupyter lab for interactive execution of notebooks
conda install -y jupyter
## Tutorials to get started# Go to tutorials directory within DISSECTcd DISSECT/tutorials
## Launch jupyter lab
jupyter notebook

GPU usage

By default, tensorflow-gpu which is installed while installed DISSECT works as long as appropriate CUDA driver is installed. DISSECT uses tensorflow-gpu version 2.7.0 with CUDA 11.2 and cuDNN 8.1. The available devices to tensorflow can be checked as below.

importtensorflowastfgpus=tf.config.list_physical_devices("GPU")
print(gpus)

This will output a list of the available GPU devices as the output below where we have 1 GPU available.

[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

In case there are multiple GPUs available, a particular GPU can be set by,

gpu_number=0# Using only the first GPUtf.config.experimental.set_visible_devices(gpus[gpu_number], 'GPU')

Tutorials

Interactive tutorials including required data are available as part of this repository at Tutorials.

  1. tutorial.ipynb: Step by step deconvolution for bulk
  2. tutorial_spatial.ipynb: Step by step deconvolution of spatial transcriptomics data (10x Visium)

A note on the supported formats

bulk RNAseq/Proteomics: A tab seperated file (gene symbols in rows, sample names in columns), or an h5ad compatible with Scanpy >=1.8.0.

scRNAseq: An h5ad compatible with Scanpy >=1.8.0. The cell metadata (.obs) must contain cell type labels (By default in column cell_type)

Spatial transcriptomics: A h5ad compatible with Scanpy >=1.8.0.

Gene ID formats should match between scRNAseq and bulk/spatial data but can be anything (HGNC symbols, ENSEMBL etc.).

The code is also available via Zenodo

To get answers quickly for a problem or feature request, please open an issue.

References

[1] Khatri, R., Machart, P. & Bonn, S. DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation. Genome Biol 25, 112 (2024). https://doi.org/10.1186/s13059-024-03251-5

About

Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/

Resources

Readme
MIT license
Activity
Custom properties

Stars

7 stars

Watchers

2 watching

Forks

3 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, '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" + '
GitHub - imsb-uke/DISSECT: Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/ · GitHub
Skip to content
BranchesTags
Repository files navigation
DISSECT
DISSECT is a deep semi-supervised learning framework that performs complete deconvolution of gene expression mixtures such as bulk RNAseq, proteomics and spatial transcriptomics. The manuscript is available at Genome Biology.
This repository contains the functions to run DISSECT interactively in Python. The code to reproduce figures and run deconvolution pipeline through comamnd line is available here.
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Folder tutorials include notebooks to get started.
Prerequisites
conda >= v22 through Anaconda or miniconda.
Installing
## Installation# Create and activate virtual environment. This is recommended to avoid conflict in dependencies.
conda create -y -n dissect python=3.9
conda activate dissect
# Clone DISSECT
git clone https://github.com/imsb-uke/DISSECT
# Install dependencies and DISSECT
pip install -r DISSECT/requirements.txt --use-deprecated=legacy-resolver
pip install DISSECT/.
# Install jupyter lab for interactive execution of notebooks
conda install -y jupyter
## Tutorials to get started# Go to tutorials directory within DISSECTcd DISSECT/tutorials
## Launch jupyter lab
jupyter notebook

GPU usage
By default, tensorflow-gpu which is installed while installed DISSECT works as long as appropriate CUDA driver is installed. DISSECT uses tensorflow-gpu version 2.7.0 with CUDA 11.2 and cuDNN 8.1. The available devices to tensorflow can be checked as below.
importtensorflowastfgpus=tf.config.list_physical_devices("GPU")
print(gpus)
This will output a list of the available GPU devices as the output below where we have 1 GPU available.
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] 
In case there are multiple GPUs available, a particular GPU can be set by,
gpu_number=0# Using only the first GPUtf.config.experimental.set_visible_devices(gpus[gpu_number], 'GPU')
Tutorials
Interactive tutorials including required data are available as part of this repository at Tutorials.
  1. tutorial.ipynb: Step by step deconvolution for bulk
  2. tutorial_spatial.ipynb: Step by step deconvolution of spatial transcriptomics data (10x Visium)
A note on the supported formats
bulk RNAseq/Proteomics: A tab seperated file (gene symbols in rows, sample names in columns), or an h5ad compatible with Scanpy >=1.8.0.
scRNAseq: An h5ad compatible with Scanpy >=1.8.0. The cell metadata (.obs) must contain cell type labels (By default in column cell_type)
Spatial transcriptomics: A h5ad compatible with Scanpy >=1.8.0.
Gene ID formats should match between scRNAseq and bulk/spatial data but can be anything (HGNC symbols, ENSEMBL etc.).
The code is also available via Zenodo
To get answers quickly for a problem or feature request, please open an issue.
References
[1] Khatri, R., Machart, P. & Bonn, S. DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation. Genome Biol 25, 112 (2024). https://doi.org/10.1186/s13059-024-03251-5
About
Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
2 watching
Forks
3 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - imsb-uke/DISSECT: Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/ · GitHub
Skip to content
BranchesTags
Repository files navigation
DISSECT
DISSECT is a deep semi-supervised learning framework that performs complete deconvolution of gene expression mixtures such as bulk RNAseq, proteomics and spatial transcriptomics. The manuscript is available at Genome Biology.
This repository contains the functions to run DISSECT interactively in Python. The code to reproduce figures and run deconvolution pipeline through comamnd line is available here.
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Folder tutorials include notebooks to get started.
Prerequisites
conda >= v22 through Anaconda or miniconda.
Installing
## Installation# Create and activate virtual environment. This is recommended to avoid conflict in dependencies.
conda create -y -n dissect python=3.9
conda activate dissect
# Clone DISSECT
git clone https://github.com/imsb-uke/DISSECT
# Install dependencies and DISSECT
pip install -r DISSECT/requirements.txt --use-deprecated=legacy-resolver
pip install DISSECT/.
# Install jupyter lab for interactive execution of notebooks
conda install -y jupyter
## Tutorials to get started# Go to tutorials directory within DISSECTcd DISSECT/tutorials
## Launch jupyter lab
jupyter notebook

GPU usage
By default, tensorflow-gpu which is installed while installed DISSECT works as long as appropriate CUDA driver is installed. DISSECT uses tensorflow-gpu version 2.7.0 with CUDA 11.2 and cuDNN 8.1. The available devices to tensorflow can be checked as below.
importtensorflowastfgpus=tf.config.list_physical_devices("GPU")
print(gpus)
This will output a list of the available GPU devices as the output below where we have 1 GPU available.
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] 
In case there are multiple GPUs available, a particular GPU can be set by,
gpu_number=0# Using only the first GPUtf.config.experimental.set_visible_devices(gpus[gpu_number], 'GPU')
Tutorials
Interactive tutorials including required data are available as part of this repository at Tutorials.
  1. tutorial.ipynb: Step by step deconvolution for bulk
  2. tutorial_spatial.ipynb: Step by step deconvolution of spatial transcriptomics data (10x Visium)
A note on the supported formats
bulk RNAseq/Proteomics: A tab seperated file (gene symbols in rows, sample names in columns), or an h5ad compatible with Scanpy >=1.8.0.
scRNAseq: An h5ad compatible with Scanpy >=1.8.0. The cell metadata (.obs) must contain cell type labels (By default in column cell_type)
Spatial transcriptomics: A h5ad compatible with Scanpy >=1.8.0.
Gene ID formats should match between scRNAseq and bulk/spatial data but can be anything (HGNC symbols, ENSEMBL etc.).
The code is also available via Zenodo
To get answers quickly for a problem or feature request, please open an issue.
References
[1] Khatri, R., Machart, P. & Bonn, S. DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation. Genome Biol 25, 112 (2024). https://doi.org/10.1186/s13059-024-03251-5
About
Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
2 watching
Forks
3 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - imsb-uke/DISSECT: Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/ · GitHub
Skip to content
BranchesTags
Repository files navigation
DISSECT
DISSECT is a deep semi-supervised learning framework that performs complete deconvolution of gene expression mixtures such as bulk RNAseq, proteomics and spatial transcriptomics. The manuscript is available at Genome Biology.
This repository contains the functions to run DISSECT interactively in Python. The code to reproduce figures and run deconvolution pipeline through comamnd line is available here.
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Folder tutorials include notebooks to get started.
Prerequisites
conda >= v22 through Anaconda or miniconda.
Installing
## Installation# Create and activate virtual environment. This is recommended to avoid conflict in dependencies.
conda create -y -n dissect python=3.9
conda activate dissect
# Clone DISSECT
git clone https://github.com/imsb-uke/DISSECT
# Install dependencies and DISSECT
pip install -r DISSECT/requirements.txt --use-deprecated=legacy-resolver
pip install DISSECT/.
# Install jupyter lab for interactive execution of notebooks
conda install -y jupyter
## Tutorials to get started# Go to tutorials directory within DISSECTcd DISSECT/tutorials
## Launch jupyter lab
jupyter notebook

GPU usage
By default, tensorflow-gpu which is installed while installed DISSECT works as long as appropriate CUDA driver is installed. DISSECT uses tensorflow-gpu version 2.7.0 with CUDA 11.2 and cuDNN 8.1. The available devices to tensorflow can be checked as below.
importtensorflowastfgpus=tf.config.list_physical_devices("GPU")
print(gpus)
This will output a list of the available GPU devices as the output below where we have 1 GPU available.
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] 
In case there are multiple GPUs available, a particular GPU can be set by,
gpu_number=0# Using only the first GPUtf.config.experimental.set_visible_devices(gpus[gpu_number], 'GPU')
Tutorials
Interactive tutorials including required data are available as part of this repository at Tutorials.
  1. tutorial.ipynb: Step by step deconvolution for bulk
  2. tutorial_spatial.ipynb: Step by step deconvolution of spatial transcriptomics data (10x Visium)
A note on the supported formats
bulk RNAseq/Proteomics: A tab seperated file (gene symbols in rows, sample names in columns), or an h5ad compatible with Scanpy >=1.8.0.
scRNAseq: An h5ad compatible with Scanpy >=1.8.0. The cell metadata (.obs) must contain cell type labels (By default in column cell_type)
Spatial transcriptomics: A h5ad compatible with Scanpy >=1.8.0.
Gene ID formats should match between scRNAseq and bulk/spatial data but can be anything (HGNC symbols, ENSEMBL etc.).
The code is also available via Zenodo
To get answers quickly for a problem or feature request, please open an issue.
References
[1] Khatri, R., Machart, P. & Bonn, S. DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation. Genome Biol 25, 112 (2024). https://doi.org/10.1186/s13059-024-03251-5
About
Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
2 watching
Forks
3 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - imsb-uke/DISSECT: Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/ · GitHub
Skip to content
BranchesTags
Repository files navigation
DISSECT
DISSECT is a deep semi-supervised learning framework that performs complete deconvolution of gene expression mixtures such as bulk RNAseq, proteomics and spatial transcriptomics. The manuscript is available at Genome Biology.
This repository contains the functions to run DISSECT interactively in Python. The code to reproduce figures and run deconvolution pipeline through comamnd line is available here.
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Folder tutorials include notebooks to get started.
Prerequisites
conda >= v22 through Anaconda or miniconda.
Installing
## Installation# Create and activate virtual environment. This is recommended to avoid conflict in dependencies.
conda create -y -n dissect python=3.9
conda activate dissect
# Clone DISSECT
git clone https://github.com/imsb-uke/DISSECT
# Install dependencies and DISSECT
pip install -r DISSECT/requirements.txt --use-deprecated=legacy-resolver
pip install DISSECT/.
# Install jupyter lab for interactive execution of notebooks
conda install -y jupyter
## Tutorials to get started# Go to tutorials directory within DISSECTcd DISSECT/tutorials
## Launch jupyter lab
jupyter notebook

GPU usage
By default, tensorflow-gpu which is installed while installed DISSECT works as long as appropriate CUDA driver is installed. DISSECT uses tensorflow-gpu version 2.7.0 with CUDA 11.2 and cuDNN 8.1. The available devices to tensorflow can be checked as below.
importtensorflowastfgpus=tf.config.list_physical_devices("GPU")
print(gpus)
This will output a list of the available GPU devices as the output below where we have 1 GPU available.
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] 
In case there are multiple GPUs available, a particular GPU can be set by,
gpu_number=0# Using only the first GPUtf.config.experimental.set_visible_devices(gpus[gpu_number], 'GPU')
Tutorials
Interactive tutorials including required data are available as part of this repository at Tutorials.
  1. tutorial.ipynb: Step by step deconvolution for bulk
  2. tutorial_spatial.ipynb: Step by step deconvolution of spatial transcriptomics data (10x Visium)
A note on the supported formats
bulk RNAseq/Proteomics: A tab seperated file (gene symbols in rows, sample names in columns), or an h5ad compatible with Scanpy >=1.8.0.
scRNAseq: An h5ad compatible with Scanpy >=1.8.0. The cell metadata (.obs) must contain cell type labels (By default in column cell_type)
Spatial transcriptomics: A h5ad compatible with Scanpy >=1.8.0.
Gene ID formats should match between scRNAseq and bulk/spatial data but can be anything (HGNC symbols, ENSEMBL etc.).
The code is also available via Zenodo
To get answers quickly for a problem or feature request, please open an issue.
References
[1] Khatri, R., Machart, P. & Bonn, S. DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation. Genome Biol 25, 112 (2024). https://doi.org/10.1186/s13059-024-03251-5
About
Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
2 watching
Forks
3 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - imsb-uke/DISSECT: Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/ · GitHub
Skip to content
BranchesTags
Repository files navigation
DISSECT
DISSECT is a deep semi-supervised learning framework that performs complete deconvolution of gene expression mixtures such as bulk RNAseq, proteomics and spatial transcriptomics. The manuscript is available at Genome Biology.
This repository contains the functions to run DISSECT interactively in Python. The code to reproduce figures and run deconvolution pipeline through comamnd line is available here.
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Folder tutorials include notebooks to get started.
Prerequisites
conda >= v22 through Anaconda or miniconda.
Installing
## Installation# Create and activate virtual environment. This is recommended to avoid conflict in dependencies.
conda create -y -n dissect python=3.9
conda activate dissect
# Clone DISSECT
git clone https://github.com/imsb-uke/DISSECT
# Install dependencies and DISSECT
pip install -r DISSECT/requirements.txt --use-deprecated=legacy-resolver
pip install DISSECT/.
# Install jupyter lab for interactive execution of notebooks
conda install -y jupyter
## Tutorials to get started# Go to tutorials directory within DISSECTcd DISSECT/tutorials
## Launch jupyter lab
jupyter notebook

GPU usage
By default, tensorflow-gpu which is installed while installed DISSECT works as long as appropriate CUDA driver is installed. DISSECT uses tensorflow-gpu version 2.7.0 with CUDA 11.2 and cuDNN 8.1. The available devices to tensorflow can be checked as below.
importtensorflowastfgpus=tf.config.list_physical_devices("GPU")
print(gpus)
This will output a list of the available GPU devices as the output below where we have 1 GPU available.
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] 
In case there are multiple GPUs available, a particular GPU can be set by,
gpu_number=0# Using only the first GPUtf.config.experimental.set_visible_devices(gpus[gpu_number], 'GPU')
Tutorials
Interactive tutorials including required data are available as part of this repository at Tutorials.
  1. tutorial.ipynb: Step by step deconvolution for bulk
  2. tutorial_spatial.ipynb: Step by step deconvolution of spatial transcriptomics data (10x Visium)
A note on the supported formats
bulk RNAseq/Proteomics: A tab seperated file (gene symbols in rows, sample names in columns), or an h5ad compatible with Scanpy >=1.8.0.
scRNAseq: An h5ad compatible with Scanpy >=1.8.0. The cell metadata (.obs) must contain cell type labels (By default in column cell_type)
Spatial transcriptomics: A h5ad compatible with Scanpy >=1.8.0.
Gene ID formats should match between scRNAseq and bulk/spatial data but can be anything (HGNC symbols, ENSEMBL etc.).
The code is also available via Zenodo
To get answers quickly for a problem or feature request, please open an issue.
References
[1] Khatri, R., Machart, P. & Bonn, S. DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation. Genome Biol 25, 112 (2024). https://doi.org/10.1186/s13059-024-03251-5
About
Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
2 watching
Forks
3 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - imsb-uke/DISSECT: Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/ · GitHub
Skip to content
BranchesTags
Repository files navigation
DISSECT
DISSECT is a deep semi-supervised learning framework that performs complete deconvolution of gene expression mixtures such as bulk RNAseq, proteomics and spatial transcriptomics. The manuscript is available at Genome Biology.
This repository contains the functions to run DISSECT interactively in Python. The code to reproduce figures and run deconvolution pipeline through comamnd line is available here.
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Folder tutorials include notebooks to get started.
Prerequisites
conda >= v22 through Anaconda or miniconda.
Installing
## Installation# Create and activate virtual environment. This is recommended to avoid conflict in dependencies.
conda create -y -n dissect python=3.9
conda activate dissect
# Clone DISSECT
git clone https://github.com/imsb-uke/DISSECT
# Install dependencies and DISSECT
pip install -r DISSECT/requirements.txt --use-deprecated=legacy-resolver
pip install DISSECT/.
# Install jupyter lab for interactive execution of notebooks
conda install -y jupyter
## Tutorials to get started# Go to tutorials directory within DISSECTcd DISSECT/tutorials
## Launch jupyter lab
jupyter notebook

GPU usage
By default, tensorflow-gpu which is installed while installed DISSECT works as long as appropriate CUDA driver is installed. DISSECT uses tensorflow-gpu version 2.7.0 with CUDA 11.2 and cuDNN 8.1. The available devices to tensorflow can be checked as below.
importtensorflowastfgpus=tf.config.list_physical_devices("GPU")
print(gpus)
This will output a list of the available GPU devices as the output below where we have 1 GPU available.
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] 
In case there are multiple GPUs available, a particular GPU can be set by,
gpu_number=0# Using only the first GPUtf.config.experimental.set_visible_devices(gpus[gpu_number], 'GPU')
Tutorials
Interactive tutorials including required data are available as part of this repository at Tutorials.
  1. tutorial.ipynb: Step by step deconvolution for bulk
  2. tutorial_spatial.ipynb: Step by step deconvolution of spatial transcriptomics data (10x Visium)
A note on the supported formats
bulk RNAseq/Proteomics: A tab seperated file (gene symbols in rows, sample names in columns), or an h5ad compatible with Scanpy >=1.8.0.
scRNAseq: An h5ad compatible with Scanpy >=1.8.0. The cell metadata (.obs) must contain cell type labels (By default in column cell_type)
Spatial transcriptomics: A h5ad compatible with Scanpy >=1.8.0.
Gene ID formats should match between scRNAseq and bulk/spatial data but can be anything (HGNC symbols, ENSEMBL etc.).
The code is also available via Zenodo
To get answers quickly for a problem or feature request, please open an issue.
References
[1] Khatri, R., Machart, P. & Bonn, S. DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation. Genome Biol 25, 112 (2024). https://doi.org/10.1186/s13059-024-03251-5
About
Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
2 watching
Forks
3 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - imsb-uke/DISSECT: Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/ · GitHub
Skip to content
BranchesTags
Repository files navigation
DISSECT
DISSECT is a deep semi-supervised learning framework that performs complete deconvolution of gene expression mixtures such as bulk RNAseq, proteomics and spatial transcriptomics. The manuscript is available at Genome Biology.
This repository contains the functions to run DISSECT interactively in Python. The code to reproduce figures and run deconvolution pipeline through comamnd line is available here.
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Folder tutorials include notebooks to get started.
Prerequisites
conda >= v22 through Anaconda or miniconda.
Installing
## Installation# Create and activate virtual environment. This is recommended to avoid conflict in dependencies.
conda create -y -n dissect python=3.9
conda activate dissect
# Clone DISSECT
git clone https://github.com/imsb-uke/DISSECT
# Install dependencies and DISSECT
pip install -r DISSECT/requirements.txt --use-deprecated=legacy-resolver
pip install DISSECT/.
# Install jupyter lab for interactive execution of notebooks
conda install -y jupyter
## Tutorials to get started# Go to tutorials directory within DISSECTcd DISSECT/tutorials
## Launch jupyter lab
jupyter notebook

GPU usage
By default, tensorflow-gpu which is installed while installed DISSECT works as long as appropriate CUDA driver is installed. DISSECT uses tensorflow-gpu version 2.7.0 with CUDA 11.2 and cuDNN 8.1. The available devices to tensorflow can be checked as below.
importtensorflowastfgpus=tf.config.list_physical_devices("GPU")
print(gpus)
This will output a list of the available GPU devices as the output below where we have 1 GPU available.
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] 
In case there are multiple GPUs available, a particular GPU can be set by,
gpu_number=0# Using only the first GPUtf.config.experimental.set_visible_devices(gpus[gpu_number], 'GPU')
Tutorials
Interactive tutorials including required data are available as part of this repository at Tutorials.
  1. tutorial.ipynb: Step by step deconvolution for bulk
  2. tutorial_spatial.ipynb: Step by step deconvolution of spatial transcriptomics data (10x Visium)
A note on the supported formats
bulk RNAseq/Proteomics: A tab seperated file (gene symbols in rows, sample names in columns), or an h5ad compatible with Scanpy >=1.8.0.
scRNAseq: An h5ad compatible with Scanpy >=1.8.0. The cell metadata (.obs) must contain cell type labels (By default in column cell_type)
Spatial transcriptomics: A h5ad compatible with Scanpy >=1.8.0.
Gene ID formats should match between scRNAseq and bulk/spatial data but can be anything (HGNC symbols, ENSEMBL etc.).
The code is also available via Zenodo
To get answers quickly for a problem or feature request, please open an issue.
References
[1] Khatri, R., Machart, P. & Bonn, S. DISSECT: deep semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation. Genome Biol 25, 112 (2024). https://doi.org/10.1186/s13059-024-03251-5
About
Deep semi-supervised complete cell deconvolution https://imsb-uke.github.io/DISSECT/
Resources
Readme
MIT license
Activity
Custom properties
Stars
7 stars
Watchers
2 watching
Forks
3 forks
Report repository
Releases
Packages
Used by
Contributors
Languages