BranchesTags

Repository files navigation

spatial-ID

python >3.8.8

Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding

Spatially resolved transcriptomics (SRT) provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state. Cell type annotation is a crucial task in the spatial transcriptome analysis of cell and tissue biology. In this study, we propose Spatial-ID, a supervision-based cell typing method, for high-throughput cell-level SRT datasets that integrates transfer learning and spatial embedding. Spatial-ID effectively incorporates the existing knowledge of reference scRNA-seq datasets and the spatial information of SRT datasets.

Dependences

numpy-1.21.3pandas-1.2.4scanpy-1.8.1torch-1.8.1torch__geometric-1.7.2

Datasets

Usage

  • Run cell_type_annotation_for_merfish.py to annotate cells in MERFISH dataset.
  • Run cell_type_annotation_for_hyp3d.py to annotate cells in MERFISH-3D dataset.
  • Run cell_type_annotation_for_slideseq.py to annotate cells in Slide-seq dataset.
  • Run cell_type_annotation_for_nanostring.py to annotate cells in NanoString dataset.

p.s. You may need to unzip dnn_model/checkpoint_Slide-seq_DM1.t7 first before running cell_type_annotation_for_slideseq.py.

[!!!] Note: An AttributeError saying that 'GELU' object has no attribute 'approximate' may occurs if your pytorch version is higher than 1.10.0. You can simply downgrade pytorch to 1.8.1 or modify the source code of pytorch temporarily.

Example

  1. Put downloaded MERFISH data (e.g. mouse1_sample1.h5ad) in "dataset/MERFISH/" (as in Line 30 of cell_type_annotation_for_merfish.py).
  2. Run cell_type_annotation_for_merfish.py to annotate cells of mouse1_sample1 data.
  3. 4 files can be found in "result/MERFISH/" (as in Line 31 of cell_type_annotation_for_merfish.py):
    • spatialID-mouse1_sample1.t7: Checkpoint of the self-supervised model in Stage 2.
    • spatialID-mouse1_sample1.h5ad: Updated H5AD file with annotation result stored.
    • spatialID-mouse1_sample1.csv: Annotation results with column "cell" representing cell IDs and "celltype_pred" representing annotated cell types.
    • spatialID-mouse1_sample1.pdf: Visualization of annotation results as shown below.

Disclaimer

This tool is for research purpose and not approved for clinical use.

This is not an official Tencent product.

Coypright

This tool is developed in Tencent AI Lab.

The copyright holder for this project is Tencent AI Lab.

All rights reserved.

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

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" + '

Skip to content
BranchesTags
Repository files navigation
spatial-ID
python >3.8.8
Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding
Spatially resolved transcriptomics (SRT) provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state. Cell type annotation is a crucial task in the spatial transcriptome analysis of cell and tissue biology. In this study, we propose Spatial-ID, a supervision-based cell typing method, for high-throughput cell-level SRT datasets that integrates transfer learning and spatial embedding. Spatial-ID effectively incorporates the existing knowledge of reference scRNA-seq datasets and the spatial information of SRT datasets.
Dependences
numpy-1.21.3pandas-1.2.4scanpy-1.8.1torch-1.8.1torch__geometric-1.7.2
Datasets
Usage
  • Run cell_type_annotation_for_merfish.py to annotate cells in MERFISH dataset.
  • Run cell_type_annotation_for_hyp3d.py to annotate cells in MERFISH-3D dataset.
  • Run cell_type_annotation_for_slideseq.py to annotate cells in Slide-seq dataset.
  • Run cell_type_annotation_for_nanostring.py to annotate cells in NanoString dataset.
p.s. You may need to unzip dnn_model/checkpoint_Slide-seq_DM1.t7 first before running cell_type_annotation_for_slideseq.py.
[!!!] Note: An AttributeError saying that 'GELU' object has no attribute 'approximate' may occurs if your pytorch version is higher than 1.10.0. You can simply downgrade pytorch to 1.8.1 or modify the source code of pytorch temporarily.
Example
  1. Put downloaded MERFISH data (e.g. mouse1_sample1.h5ad) in "dataset/MERFISH/" (as in Line 30 of cell_type_annotation_for_merfish.py).
  2. Run cell_type_annotation_for_merfish.py to annotate cells of mouse1_sample1 data.
  3. 4 files can be found in "result/MERFISH/" (as in Line 31 of cell_type_annotation_for_merfish.py):

    • spatialID-mouse1_sample1.t7: Checkpoint of the self-supervised model in Stage 2.
    • spatialID-mouse1_sample1.h5ad: Updated H5AD file with annotation result stored.
    • spatialID-mouse1_sample1.csv: Annotation results with column "cell" representing cell IDs and "celltype_pred" representing annotated cell types.
    • spatialID-mouse1_sample1.pdf: Visualization of annotation results as shown below.
Disclaimer
This tool is for research purpose and not approved for clinical use.
This is not an official Tencent product.
Coypright
This tool is developed in Tencent AI Lab.
The copyright holder for this project is Tencent AI Lab.
All rights reserved.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
spatial-ID
python >3.8.8
Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding
Spatially resolved transcriptomics (SRT) provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state. Cell type annotation is a crucial task in the spatial transcriptome analysis of cell and tissue biology. In this study, we propose Spatial-ID, a supervision-based cell typing method, for high-throughput cell-level SRT datasets that integrates transfer learning and spatial embedding. Spatial-ID effectively incorporates the existing knowledge of reference scRNA-seq datasets and the spatial information of SRT datasets.
Dependences
numpy-1.21.3pandas-1.2.4scanpy-1.8.1torch-1.8.1torch__geometric-1.7.2
Datasets
Usage
  • Run cell_type_annotation_for_merfish.py to annotate cells in MERFISH dataset.
  • Run cell_type_annotation_for_hyp3d.py to annotate cells in MERFISH-3D dataset.
  • Run cell_type_annotation_for_slideseq.py to annotate cells in Slide-seq dataset.
  • Run cell_type_annotation_for_nanostring.py to annotate cells in NanoString dataset.
p.s. You may need to unzip dnn_model/checkpoint_Slide-seq_DM1.t7 first before running cell_type_annotation_for_slideseq.py.
[!!!] Note: An AttributeError saying that 'GELU' object has no attribute 'approximate' may occurs if your pytorch version is higher than 1.10.0. You can simply downgrade pytorch to 1.8.1 or modify the source code of pytorch temporarily.
Example
  1. Put downloaded MERFISH data (e.g. mouse1_sample1.h5ad) in "dataset/MERFISH/" (as in Line 30 of cell_type_annotation_for_merfish.py).
  2. Run cell_type_annotation_for_merfish.py to annotate cells of mouse1_sample1 data.
  3. 4 files can be found in "result/MERFISH/" (as in Line 31 of cell_type_annotation_for_merfish.py):

    • spatialID-mouse1_sample1.t7: Checkpoint of the self-supervised model in Stage 2.
    • spatialID-mouse1_sample1.h5ad: Updated H5AD file with annotation result stored.
    • spatialID-mouse1_sample1.csv: Annotation results with column "cell" representing cell IDs and "celltype_pred" representing annotated cell types.
    • spatialID-mouse1_sample1.pdf: Visualization of annotation results as shown below.
Disclaimer
This tool is for research purpose and not approved for clinical use.
This is not an official Tencent product.
Coypright
This tool is developed in Tencent AI Lab.
The copyright holder for this project is Tencent AI Lab.
All rights reserved.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
spatial-ID
python >3.8.8
Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding
Spatially resolved transcriptomics (SRT) provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state. Cell type annotation is a crucial task in the spatial transcriptome analysis of cell and tissue biology. In this study, we propose Spatial-ID, a supervision-based cell typing method, for high-throughput cell-level SRT datasets that integrates transfer learning and spatial embedding. Spatial-ID effectively incorporates the existing knowledge of reference scRNA-seq datasets and the spatial information of SRT datasets.
Dependences
numpy-1.21.3pandas-1.2.4scanpy-1.8.1torch-1.8.1torch__geometric-1.7.2
Datasets
Usage
  • Run cell_type_annotation_for_merfish.py to annotate cells in MERFISH dataset.
  • Run cell_type_annotation_for_hyp3d.py to annotate cells in MERFISH-3D dataset.
  • Run cell_type_annotation_for_slideseq.py to annotate cells in Slide-seq dataset.
  • Run cell_type_annotation_for_nanostring.py to annotate cells in NanoString dataset.
p.s. You may need to unzip dnn_model/checkpoint_Slide-seq_DM1.t7 first before running cell_type_annotation_for_slideseq.py.
[!!!] Note: An AttributeError saying that 'GELU' object has no attribute 'approximate' may occurs if your pytorch version is higher than 1.10.0. You can simply downgrade pytorch to 1.8.1 or modify the source code of pytorch temporarily.
Example
  1. Put downloaded MERFISH data (e.g. mouse1_sample1.h5ad) in "dataset/MERFISH/" (as in Line 30 of cell_type_annotation_for_merfish.py).
  2. Run cell_type_annotation_for_merfish.py to annotate cells of mouse1_sample1 data.
  3. 4 files can be found in "result/MERFISH/" (as in Line 31 of cell_type_annotation_for_merfish.py):

    • spatialID-mouse1_sample1.t7: Checkpoint of the self-supervised model in Stage 2.
    • spatialID-mouse1_sample1.h5ad: Updated H5AD file with annotation result stored.
    • spatialID-mouse1_sample1.csv: Annotation results with column "cell" representing cell IDs and "celltype_pred" representing annotated cell types.
    • spatialID-mouse1_sample1.pdf: Visualization of annotation results as shown below.
Disclaimer
This tool is for research purpose and not approved for clinical use.
This is not an official Tencent product.
Coypright
This tool is developed in Tencent AI Lab.
The copyright holder for this project is Tencent AI Lab.
All rights reserved.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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" + '

Skip to content
BranchesTags
Repository files navigation
spatial-ID
python >3.8.8
Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding
Spatially resolved transcriptomics (SRT) provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state. Cell type annotation is a crucial task in the spatial transcriptome analysis of cell and tissue biology. In this study, we propose Spatial-ID, a supervision-based cell typing method, for high-throughput cell-level SRT datasets that integrates transfer learning and spatial embedding. Spatial-ID effectively incorporates the existing knowledge of reference scRNA-seq datasets and the spatial information of SRT datasets.
Dependences
numpy-1.21.3pandas-1.2.4scanpy-1.8.1torch-1.8.1torch__geometric-1.7.2
Datasets
Usage
  • Run cell_type_annotation_for_merfish.py to annotate cells in MERFISH dataset.
  • Run cell_type_annotation_for_hyp3d.py to annotate cells in MERFISH-3D dataset.
  • Run cell_type_annotation_for_slideseq.py to annotate cells in Slide-seq dataset.
  • Run cell_type_annotation_for_nanostring.py to annotate cells in NanoString dataset.
p.s. You may need to unzip dnn_model/checkpoint_Slide-seq_DM1.t7 first before running cell_type_annotation_for_slideseq.py.
[!!!] Note: An AttributeError saying that 'GELU' object has no attribute 'approximate' may occurs if your pytorch version is higher than 1.10.0. You can simply downgrade pytorch to 1.8.1 or modify the source code of pytorch temporarily.
Example
  1. Put downloaded MERFISH data (e.g. mouse1_sample1.h5ad) in "dataset/MERFISH/" (as in Line 30 of cell_type_annotation_for_merfish.py).
  2. Run cell_type_annotation_for_merfish.py to annotate cells of mouse1_sample1 data.
  3. 4 files can be found in "result/MERFISH/" (as in Line 31 of cell_type_annotation_for_merfish.py):

    • spatialID-mouse1_sample1.t7: Checkpoint of the self-supervised model in Stage 2.
    • spatialID-mouse1_sample1.h5ad: Updated H5AD file with annotation result stored.
    • spatialID-mouse1_sample1.csv: Annotation results with column "cell" representing cell IDs and "celltype_pred" representing annotated cell types.
    • spatialID-mouse1_sample1.pdf: Visualization of annotation results as shown below.
Disclaimer
This tool is for research purpose and not approved for clinical use.
This is not an official Tencent product.
Coypright
This tool is developed in Tencent AI Lab.
The copyright holder for this project is Tencent AI Lab.
All rights reserved.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
spatial-ID
python >3.8.8
Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding
Spatially resolved transcriptomics (SRT) provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state. Cell type annotation is a crucial task in the spatial transcriptome analysis of cell and tissue biology. In this study, we propose Spatial-ID, a supervision-based cell typing method, for high-throughput cell-level SRT datasets that integrates transfer learning and spatial embedding. Spatial-ID effectively incorporates the existing knowledge of reference scRNA-seq datasets and the spatial information of SRT datasets.
Dependences
numpy-1.21.3pandas-1.2.4scanpy-1.8.1torch-1.8.1torch__geometric-1.7.2
Datasets
Usage
  • Run cell_type_annotation_for_merfish.py to annotate cells in MERFISH dataset.
  • Run cell_type_annotation_for_hyp3d.py to annotate cells in MERFISH-3D dataset.
  • Run cell_type_annotation_for_slideseq.py to annotate cells in Slide-seq dataset.
  • Run cell_type_annotation_for_nanostring.py to annotate cells in NanoString dataset.
p.s. You may need to unzip dnn_model/checkpoint_Slide-seq_DM1.t7 first before running cell_type_annotation_for_slideseq.py.
[!!!] Note: An AttributeError saying that 'GELU' object has no attribute 'approximate' may occurs if your pytorch version is higher than 1.10.0. You can simply downgrade pytorch to 1.8.1 or modify the source code of pytorch temporarily.
Example
  1. Put downloaded MERFISH data (e.g. mouse1_sample1.h5ad) in "dataset/MERFISH/" (as in Line 30 of cell_type_annotation_for_merfish.py).
  2. Run cell_type_annotation_for_merfish.py to annotate cells of mouse1_sample1 data.
  3. 4 files can be found in "result/MERFISH/" (as in Line 31 of cell_type_annotation_for_merfish.py):

    • spatialID-mouse1_sample1.t7: Checkpoint of the self-supervised model in Stage 2.
    • spatialID-mouse1_sample1.h5ad: Updated H5AD file with annotation result stored.
    • spatialID-mouse1_sample1.csv: Annotation results with column "cell" representing cell IDs and "celltype_pred" representing annotated cell types.
    • spatialID-mouse1_sample1.pdf: Visualization of annotation results as shown below.
Disclaimer
This tool is for research purpose and not approved for clinical use.
This is not an official Tencent product.
Coypright
This tool is developed in Tencent AI Lab.
The copyright holder for this project is Tencent AI Lab.
All rights reserved.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
spatial-ID
python >3.8.8
Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding
Spatially resolved transcriptomics (SRT) provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state. Cell type annotation is a crucial task in the spatial transcriptome analysis of cell and tissue biology. In this study, we propose Spatial-ID, a supervision-based cell typing method, for high-throughput cell-level SRT datasets that integrates transfer learning and spatial embedding. Spatial-ID effectively incorporates the existing knowledge of reference scRNA-seq datasets and the spatial information of SRT datasets.
Dependences
numpy-1.21.3pandas-1.2.4scanpy-1.8.1torch-1.8.1torch__geometric-1.7.2
Datasets
Usage
  • Run cell_type_annotation_for_merfish.py to annotate cells in MERFISH dataset.
  • Run cell_type_annotation_for_hyp3d.py to annotate cells in MERFISH-3D dataset.
  • Run cell_type_annotation_for_slideseq.py to annotate cells in Slide-seq dataset.
  • Run cell_type_annotation_for_nanostring.py to annotate cells in NanoString dataset.
p.s. You may need to unzip dnn_model/checkpoint_Slide-seq_DM1.t7 first before running cell_type_annotation_for_slideseq.py.
[!!!] Note: An AttributeError saying that 'GELU' object has no attribute 'approximate' may occurs if your pytorch version is higher than 1.10.0. You can simply downgrade pytorch to 1.8.1 or modify the source code of pytorch temporarily.
Example
  1. Put downloaded MERFISH data (e.g. mouse1_sample1.h5ad) in "dataset/MERFISH/" (as in Line 30 of cell_type_annotation_for_merfish.py).
  2. Run cell_type_annotation_for_merfish.py to annotate cells of mouse1_sample1 data.
  3. 4 files can be found in "result/MERFISH/" (as in Line 31 of cell_type_annotation_for_merfish.py):

    • spatialID-mouse1_sample1.t7: Checkpoint of the self-supervised model in Stage 2.
    • spatialID-mouse1_sample1.h5ad: Updated H5AD file with annotation result stored.
    • spatialID-mouse1_sample1.csv: Annotation results with column "cell" representing cell IDs and "celltype_pred" representing annotated cell types.
    • spatialID-mouse1_sample1.pdf: Visualization of annotation results as shown below.
Disclaimer
This tool is for research purpose and not approved for clinical use.
This is not an official Tencent product.
Coypright
This tool is developed in Tencent AI Lab.
The copyright holder for this project is Tencent AI Lab.
All rights reserved.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
spatial-ID
python >3.8.8
Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding
Spatially resolved transcriptomics (SRT) provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state. Cell type annotation is a crucial task in the spatial transcriptome analysis of cell and tissue biology. In this study, we propose Spatial-ID, a supervision-based cell typing method, for high-throughput cell-level SRT datasets that integrates transfer learning and spatial embedding. Spatial-ID effectively incorporates the existing knowledge of reference scRNA-seq datasets and the spatial information of SRT datasets.
Dependences
numpy-1.21.3pandas-1.2.4scanpy-1.8.1torch-1.8.1torch__geometric-1.7.2
Datasets
Usage
  • Run cell_type_annotation_for_merfish.py to annotate cells in MERFISH dataset.
  • Run cell_type_annotation_for_hyp3d.py to annotate cells in MERFISH-3D dataset.
  • Run cell_type_annotation_for_slideseq.py to annotate cells in Slide-seq dataset.
  • Run cell_type_annotation_for_nanostring.py to annotate cells in NanoString dataset.
p.s. You may need to unzip dnn_model/checkpoint_Slide-seq_DM1.t7 first before running cell_type_annotation_for_slideseq.py.
[!!!] Note: An AttributeError saying that 'GELU' object has no attribute 'approximate' may occurs if your pytorch version is higher than 1.10.0. You can simply downgrade pytorch to 1.8.1 or modify the source code of pytorch temporarily.
Example
  1. Put downloaded MERFISH data (e.g. mouse1_sample1.h5ad) in "dataset/MERFISH/" (as in Line 30 of cell_type_annotation_for_merfish.py).
  2. Run cell_type_annotation_for_merfish.py to annotate cells of mouse1_sample1 data.
  3. 4 files can be found in "result/MERFISH/" (as in Line 31 of cell_type_annotation_for_merfish.py):

    • spatialID-mouse1_sample1.t7: Checkpoint of the self-supervised model in Stage 2.
    • spatialID-mouse1_sample1.h5ad: Updated H5AD file with annotation result stored.
    • spatialID-mouse1_sample1.csv: Annotation results with column "cell" representing cell IDs and "celltype_pred" representing annotated cell types.
    • spatialID-mouse1_sample1.pdf: Visualization of annotation results as shown below.
Disclaimer
This tool is for research purpose and not approved for clinical use.
This is not an official Tencent product.
Coypright
This tool is developed in Tencent AI Lab.
The copyright holder for this project is Tencent AI Lab.
All rights reserved.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages