https://github.com/timtreis/202504_workshop_gscn
https://github.com/timtreis/202504_workshop_gscn
Last synced: over 1 year ago
JSON representation
- Host: GitHub
- URL: https://github.com/timtreis/202504_workshop_gscn
- Owner: timtreis
- Created: 2025-04-06T15:22:44.000Z (over 1 year ago)
- Default Branch: gh-pages
- Last Pushed: 2025-04-06T15:45:13.000Z (over 1 year ago)
- Last Synced: 2025-04-06T16:35:37.836Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 54.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.html
Awesome Lists containing this project
README
scverse workshop in collaboration with the GSCN — GSCN Workshop 2025
document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
let toggleHintShow = 'Click to show';
let toggleHintHide = 'Click to hide';
let toggleOpenOnPrint = 'true';
var togglebuttonSelector = '.toggle, .admonition.dropdown';
const THEBE_JS_URL = "https://unpkg.com/thebe@0.8.2/lib/index.js"; const thebe_selector = ".thebe,.cell"; const thebe_selector_input = "pre"; const thebe_selector_output = ".output, .cell_output"
var togglebuttonSelector = '.toggle, .admonition.dropdown';
const THEBE_JS_URL = "https://unpkg.com/thebe@0.8.2/lib/index.js"; const thebe_selector = ".thebe,.cell"; const thebe_selector_input = "pre"; const thebe_selector_output = ".output, .cell_output"
window.MathJax = {"options": {"processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}
DOCUMENTATION_OPTIONS.pagename = 'README';
Back to top
scverse workshop in collaboration with the GSCN#
Welcome to the GSCN Workshop materials! This book contains the workshop materials organized by day:
Each day contains its own set of notebooks and materials for the workshop.
Before the event#
Tech setup#
In addition to the actual workshop, we offer two pre-workshop sessions designed to help participants with any issues installing software required for the workshop. During these, we also encourage participants to ask questions about basic Python/Jupyter Notebook usages (the notebook discussed in these sessions can be found here). The two sessions take place on:
Wednesday (02.04.2025), 15:00 - 17:00 CET
Thursday (03.04.2025), 15:30 - 17:30 CET
The Zoom links to these events can be found in the mail sent on Sunday, 30.03.2025. If you have any other questions, please direct them to the 2025-04: Workshop GSCN > pre-workshop support Zulip channel.
Syllabus#
Note: Schedule subject to minor adjustments.
Date
Time
Activity
Monday, 07.04.2025
09:00 – 09:15
Welcome & Introduction to the Workshop
09:15 – 10:00
Talk: “An in-depth look at AnnData” by Ilan Gold
10:00 – 11:30
1) Basic pre-processing of scRNA-seq data
2) Batch effect correction
3) Downstream analysis
11:30 - 12:00
Hands-on coding & Troubleshooting
12:00 – 13:00
Lunch
13:00 – 13:30
Intro to “Trajectory analysis”
13:30 – 13:40
Break
13:40 – 15:20
Trajectory analysis
15:20 – 15:40
Break
15:40 – 17:30
Trajectory analysis
19:00 - tbd
Dinner at Porta Nova (Robert-Koch-Platz 12)
Tuesday, 08.04.2025
09:00 – 09:30
Recap and Q&A, tech setup for spatial workshop
09:30 – 10:00
Talk: “Clonal dynamics and mitochondrial genetics through the
lens of single-cell multi-omics” by Leif Ludwig
10:00 – 11:30
1) Ligand-receptor interaction analysis
2) Differential cellular abundance
11:30 - 12:00
Hands-on coding & Troubleshooting
12:00 – 13:00
Lunch
13:00 – 13:30
Intro to “Spatial Omics”
13:30 – 13:40
Break
13:40 – 15:20
Spatial preprocessing & analysis
15:20 – 15:40
Break
15:40 – 17:30
Spatial preprocessing & analysis
20:00 - tbd
Dinner (self-organised)
Wednesday, 09.042025
09:00 – 09:30
Talk: “Spatiotemporal dynamics of tumor development in a novel
triple-negative breast cancer model” by Tancredi Massimo Pentimalli
09:30 – 11:30
Talk: “Foxa2 pioneers enteroendocrine lineage recruitment from
intestinal stem cells” by Michael Sterr
10:00 – 11:30
Option A) Reproduce published figures
Option B) Discuss your own data/analysis
11:30 – 12:00
Results / Feedback
{
requestKernel: true,
binderOptions: {
repo: "binder-examples/jupyter-stacks-datascience",
ref: "master",
},
codeMirrorConfig: {
theme: "abcdef",
mode: "python"
},
kernelOptions: {
name: "python3",
path: "./."
},
predefinedOutput: true
}
kernelName = 'python3'