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Course materials for Single Cell Analysis in Python 2024, EBI

Trainers: Iris Yu, Jiawei Wang, Anna Vathrakokili, Yuyao Song, Andrian Yang, Nadav Yayon

Contributor: Hugo Tavares

Setting up the environment

To run the scripts for generating counts from raw fastq files Demonstrations/01_*.sh, please install CellRanger using these instructions.

To run all the other demo notebooks, follow the instructions below. It walks you through creating and using a container that has all the packages needed to run the demo notebooks.

  1. Install Docker or Singularity
    • Singularity is best for an HPC environment as a regular HPC user. Check if your institute's HPC already has it.
    • Docker might be easier to install in your local machine, depending on your machine.
  2. Pull the prebuilt training image that Andrian Yang built, here. This image is based on the official scanpy image gcfntnu:/scanpy:latest.
    • If using Signularity, do:
    singularity pull scrnaseq2024.sif docker://ghcr.io/andr-kun/scrnaseq2024-container:latest
    where scrnaseq2024.sif is the name you would like to save the image file as.
    • If using Docker, do:
    docker pull ghcr.io/andr-kun/scrnaseq2024-container:latest
  3. Run Jupyter lab using the image you built, either using Docker or Singularity. If Singularity, you run it the same way we call it during the course sessions.
    • Using Singularity, run the command below while standing in this repo's root directory, or whichever directory you would like to be the root of Jupyter when it initialises.
    singularity exec scrnaseq2024.sif jupyter lab
    • Using Docker, run the command below while standing in this repo's root directory. The argument -v "$(pwd):$(pwd)" will bind mount the present working directory to the container such that you can call it as is, and -w "$(pwd)" will set the present working directory as the working directory in the container. Jupyter will initialise with the present working diretory as the Jupyter root.
    docker run -p 8888:8888 -v "$(pwd):$(pwd)" -w "$(pwd)" ghcr.io/andr-kun/scrnaseq2024-container:latest jupyter lab --ip 0.0.0.0 --allow-root

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Course materials for Single Cell Analysis in Python 2024, EBI
Trainers: Iris Yu, Jiawei Wang, Anna Vathrakokili, Yuyao Song, Andrian Yang, Nadav Yayon
Contributor: Hugo Tavares
Setting up the environment
To run the scripts for generating counts from raw fastq files Demonstrations/01_*.sh, please install CellRanger using these instructions.
To run all the other demo notebooks, follow the instructions below. It walks you through creating and using a container that has all the packages needed to run the demo notebooks.
  1. Install Docker or Singularity
    • Singularity is best for an HPC environment as a regular HPC user. Check if your institute's HPC already has it.
    • Docker might be easier to install in your local machine, depending on your machine.
  2. Pull the prebuilt training image that Andrian Yang built, here. This image is based on the official scanpy image gcfntnu:/scanpy:latest.

    • If using Signularity, do:
    singularity pull scrnaseq2024.sif docker://ghcr.io/andr-kun/scrnaseq2024-container:latest

    
where scrnaseq2024.sif is the name you would like to save the image file as.

    • If using Docker, do:
    docker pull ghcr.io/andr-kun/scrnaseq2024-container:latest

  3. Run Jupyter lab using the image you built, either using Docker or Singularity. If Singularity, you run it the same way we call it during the course sessions.

    • Using Singularity, run the command below while standing in this repo's root directory, or whichever directory you would like to be the root of Jupyter when it initialises.
    singularity exec scrnaseq2024.sif jupyter lab

    • Using Docker, run the command below while standing in this repo's root directory. The argument -v "$(pwd):$(pwd)" will bind mount the present working directory to the container such that you can call it as is, and -w "$(pwd)" will set the present working directory as the working directory in the container. Jupyter will initialise with the present working diretory as the Jupyter root.
    docker run -p 8888:8888 -v "$(pwd):$(pwd)" -w "$(pwd)" ghcr.io/andr-kun/scrnaseq2024-container:latest jupyter lab --ip 0.0.0.0 --allow-root

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Course materials for EBI course scRNA-seq analysis in Python, 2024
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8 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Course materials for Single Cell Analysis in Python 2024, EBI
Trainers: Iris Yu, Jiawei Wang, Anna Vathrakokili, Yuyao Song, Andrian Yang, Nadav Yayon
Contributor: Hugo Tavares
Setting up the environment
To run the scripts for generating counts from raw fastq files Demonstrations/01_*.sh, please install CellRanger using these instructions.
To run all the other demo notebooks, follow the instructions below. It walks you through creating and using a container that has all the packages needed to run the demo notebooks.
  1. Install Docker or Singularity
    • Singularity is best for an HPC environment as a regular HPC user. Check if your institute's HPC already has it.
    • Docker might be easier to install in your local machine, depending on your machine.
  2. Pull the prebuilt training image that Andrian Yang built, here. This image is based on the official scanpy image gcfntnu:/scanpy:latest.

    • If using Signularity, do:
    singularity pull scrnaseq2024.sif docker://ghcr.io/andr-kun/scrnaseq2024-container:latest

    
where scrnaseq2024.sif is the name you would like to save the image file as.

    • If using Docker, do:
    docker pull ghcr.io/andr-kun/scrnaseq2024-container:latest

  3. Run Jupyter lab using the image you built, either using Docker or Singularity. If Singularity, you run it the same way we call it during the course sessions.

    • Using Singularity, run the command below while standing in this repo's root directory, or whichever directory you would like to be the root of Jupyter when it initialises.
    singularity exec scrnaseq2024.sif jupyter lab

    • Using Docker, run the command below while standing in this repo's root directory. The argument -v "$(pwd):$(pwd)" will bind mount the present working directory to the container such that you can call it as is, and -w "$(pwd)" will set the present working directory as the working directory in the container. Jupyter will initialise with the present working diretory as the Jupyter root.
    docker run -p 8888:8888 -v "$(pwd):$(pwd)" -w "$(pwd)" ghcr.io/andr-kun/scrnaseq2024-container:latest jupyter lab --ip 0.0.0.0 --allow-root

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Course materials for EBI course scRNA-seq analysis in Python, 2024
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Forks
8 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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README.md
Repository files navigation
Course materials for Single Cell Analysis in Python 2024, EBI
Trainers: Iris Yu, Jiawei Wang, Anna Vathrakokili, Yuyao Song, Andrian Yang, Nadav Yayon
Contributor: Hugo Tavares
Setting up the environment
To run the scripts for generating counts from raw fastq files Demonstrations/01_*.sh, please install CellRanger using these instructions.
To run all the other demo notebooks, follow the instructions below. It walks you through creating and using a container that has all the packages needed to run the demo notebooks.
  1. Install Docker or Singularity
    • Singularity is best for an HPC environment as a regular HPC user. Check if your institute's HPC already has it.
    • Docker might be easier to install in your local machine, depending on your machine.
  2. Pull the prebuilt training image that Andrian Yang built, here. This image is based on the official scanpy image gcfntnu:/scanpy:latest.

    • If using Signularity, do:
    singularity pull scrnaseq2024.sif docker://ghcr.io/andr-kun/scrnaseq2024-container:latest

    
where scrnaseq2024.sif is the name you would like to save the image file as.

    • If using Docker, do:
    docker pull ghcr.io/andr-kun/scrnaseq2024-container:latest

  3. Run Jupyter lab using the image you built, either using Docker or Singularity. If Singularity, you run it the same way we call it during the course sessions.

    • Using Singularity, run the command below while standing in this repo's root directory, or whichever directory you would like to be the root of Jupyter when it initialises.
    singularity exec scrnaseq2024.sif jupyter lab

    • Using Docker, run the command below while standing in this repo's root directory. The argument -v "$(pwd):$(pwd)" will bind mount the present working directory to the container such that you can call it as is, and -w "$(pwd)" will set the present working directory as the working directory in the container. Jupyter will initialise with the present working diretory as the Jupyter root.
    docker run -p 8888:8888 -v "$(pwd):$(pwd)" -w "$(pwd)" ghcr.io/andr-kun/scrnaseq2024-container:latest jupyter lab --ip 0.0.0.0 --allow-root

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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
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59 Commits
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README.md
Repository files navigation
Course materials for Single Cell Analysis in Python 2024, EBI
Trainers: Iris Yu, Jiawei Wang, Anna Vathrakokili, Yuyao Song, Andrian Yang, Nadav Yayon
Contributor: Hugo Tavares
Setting up the environment
To run the scripts for generating counts from raw fastq files Demonstrations/01_*.sh, please install CellRanger using these instructions.
To run all the other demo notebooks, follow the instructions below. It walks you through creating and using a container that has all the packages needed to run the demo notebooks.
  1. Install Docker or Singularity
    • Singularity is best for an HPC environment as a regular HPC user. Check if your institute's HPC already has it.
    • Docker might be easier to install in your local machine, depending on your machine.
  2. Pull the prebuilt training image that Andrian Yang built, here. This image is based on the official scanpy image gcfntnu:/scanpy:latest.

    • If using Signularity, do:
    singularity pull scrnaseq2024.sif docker://ghcr.io/andr-kun/scrnaseq2024-container:latest

    
where scrnaseq2024.sif is the name you would like to save the image file as.

    • If using Docker, do:
    docker pull ghcr.io/andr-kun/scrnaseq2024-container:latest

  3. Run Jupyter lab using the image you built, either using Docker or Singularity. If Singularity, you run it the same way we call it during the course sessions.

    • Using Singularity, run the command below while standing in this repo's root directory, or whichever directory you would like to be the root of Jupyter when it initialises.
    singularity exec scrnaseq2024.sif jupyter lab

    • Using Docker, run the command below while standing in this repo's root directory. The argument -v "$(pwd):$(pwd)" will bind mount the present working directory to the container such that you can call it as is, and -w "$(pwd)" will set the present working directory as the working directory in the container. Jupyter will initialise with the present working diretory as the Jupyter root.
    docker run -p 8888:8888 -v "$(pwd):$(pwd)" -w "$(pwd)" ghcr.io/andr-kun/scrnaseq2024-container:latest jupyter lab --ip 0.0.0.0 --allow-root

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Course materials for EBI course scRNA-seq analysis in Python, 2024
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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59 Commits
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Repository files navigation
Course materials for Single Cell Analysis in Python 2024, EBI
Trainers: Iris Yu, Jiawei Wang, Anna Vathrakokili, Yuyao Song, Andrian Yang, Nadav Yayon
Contributor: Hugo Tavares
Setting up the environment
To run the scripts for generating counts from raw fastq files Demonstrations/01_*.sh, please install CellRanger using these instructions.
To run all the other demo notebooks, follow the instructions below. It walks you through creating and using a container that has all the packages needed to run the demo notebooks.
  1. Install Docker or Singularity
    • Singularity is best for an HPC environment as a regular HPC user. Check if your institute's HPC already has it.
    • Docker might be easier to install in your local machine, depending on your machine.
  2. Pull the prebuilt training image that Andrian Yang built, here. This image is based on the official scanpy image gcfntnu:/scanpy:latest.

    • If using Signularity, do:
    singularity pull scrnaseq2024.sif docker://ghcr.io/andr-kun/scrnaseq2024-container:latest

    
where scrnaseq2024.sif is the name you would like to save the image file as.

    • If using Docker, do:
    docker pull ghcr.io/andr-kun/scrnaseq2024-container:latest

  3. Run Jupyter lab using the image you built, either using Docker or Singularity. If Singularity, you run it the same way we call it during the course sessions.

    • Using Singularity, run the command below while standing in this repo's root directory, or whichever directory you would like to be the root of Jupyter when it initialises.
    singularity exec scrnaseq2024.sif jupyter lab

    • Using Docker, run the command below while standing in this repo's root directory. The argument -v "$(pwd):$(pwd)" will bind mount the present working directory to the container such that you can call it as is, and -w "$(pwd)" will set the present working directory as the working directory in the container. Jupyter will initialise with the present working diretory as the Jupyter root.
    docker run -p 8888:8888 -v "$(pwd):$(pwd)" -w "$(pwd)" ghcr.io/andr-kun/scrnaseq2024-container:latest jupyter lab --ip 0.0.0.0 --allow-root

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Course materials for EBI course scRNA-seq analysis in Python, 2024
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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59 Commits
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README.md
Repository files navigation
Course materials for Single Cell Analysis in Python 2024, EBI
Trainers: Iris Yu, Jiawei Wang, Anna Vathrakokili, Yuyao Song, Andrian Yang, Nadav Yayon
Contributor: Hugo Tavares
Setting up the environment
To run the scripts for generating counts from raw fastq files Demonstrations/01_*.sh, please install CellRanger using these instructions.
To run all the other demo notebooks, follow the instructions below. It walks you through creating and using a container that has all the packages needed to run the demo notebooks.
  1. Install Docker or Singularity
    • Singularity is best for an HPC environment as a regular HPC user. Check if your institute's HPC already has it.
    • Docker might be easier to install in your local machine, depending on your machine.
  2. Pull the prebuilt training image that Andrian Yang built, here. This image is based on the official scanpy image gcfntnu:/scanpy:latest.

    • If using Signularity, do:
    singularity pull scrnaseq2024.sif docker://ghcr.io/andr-kun/scrnaseq2024-container:latest

    
where scrnaseq2024.sif is the name you would like to save the image file as.

    • If using Docker, do:
    docker pull ghcr.io/andr-kun/scrnaseq2024-container:latest

  3. Run Jupyter lab using the image you built, either using Docker or Singularity. If Singularity, you run it the same way we call it during the course sessions.

    • Using Singularity, run the command below while standing in this repo's root directory, or whichever directory you would like to be the root of Jupyter when it initialises.
    singularity exec scrnaseq2024.sif jupyter lab

    • Using Docker, run the command below while standing in this repo's root directory. The argument -v "$(pwd):$(pwd)" will bind mount the present working directory to the container such that you can call it as is, and -w "$(pwd)" will set the present working directory as the working directory in the container. Jupyter will initialise with the present working diretory as the Jupyter root.
    docker run -p 8888:8888 -v "$(pwd):$(pwd)" -w "$(pwd)" ghcr.io/andr-kun/scrnaseq2024-container:latest jupyter lab --ip 0.0.0.0 --allow-root

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Course materials for Single Cell Analysis in Python 2024, EBI
Trainers: Iris Yu, Jiawei Wang, Anna Vathrakokili, Yuyao Song, Andrian Yang, Nadav Yayon
Contributor: Hugo Tavares
Setting up the environment
To run the scripts for generating counts from raw fastq files Demonstrations/01_*.sh, please install CellRanger using these instructions.
To run all the other demo notebooks, follow the instructions below. It walks you through creating and using a container that has all the packages needed to run the demo notebooks.
  1. Install Docker or Singularity
    • Singularity is best for an HPC environment as a regular HPC user. Check if your institute's HPC already has it.
    • Docker might be easier to install in your local machine, depending on your machine.
  2. Pull the prebuilt training image that Andrian Yang built, here. This image is based on the official scanpy image gcfntnu:/scanpy:latest.

    • If using Signularity, do:
    singularity pull scrnaseq2024.sif docker://ghcr.io/andr-kun/scrnaseq2024-container:latest

    
where scrnaseq2024.sif is the name you would like to save the image file as.

    • If using Docker, do:
    docker pull ghcr.io/andr-kun/scrnaseq2024-container:latest

  3. Run Jupyter lab using the image you built, either using Docker or Singularity. If Singularity, you run it the same way we call it during the course sessions.

    • Using Singularity, run the command below while standing in this repo's root directory, or whichever directory you would like to be the root of Jupyter when it initialises.
    singularity exec scrnaseq2024.sif jupyter lab

    • Using Docker, run the command below while standing in this repo's root directory. The argument -v "$(pwd):$(pwd)" will bind mount the present working directory to the container such that you can call it as is, and -w "$(pwd)" will set the present working directory as the working directory in the container. Jupyter will initialise with the present working diretory as the Jupyter root.
    docker run -p 8888:8888 -v "$(pwd):$(pwd)" -w "$(pwd)" ghcr.io/andr-kun/scrnaseq2024-container:latest jupyter lab --ip 0.0.0.0 --allow-root

About
Course materials for EBI course scRNA-seq analysis in Python, 2024
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8 forks
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