AlexsLemonade / AlexsLemonade/OpenScPCA-analysis
Initiation of SCPCP000001
- Dominant language
- HTML
- Stars
- 16
- Forks
- 24
- Avg merge
- 3d 5h
- Merged PRs (30d)
- 4
Description
### Please link to the GitHub Discussion for this proposed analysis.
https://github.com/AlexsLemonade/OpenScPCA-analysis/discussions/722
### Describe the goals of this analysis module.
To use CellTypist to transfer GBM labels from the Core GBMap ([https://www.biorxiv.org/content/10.1101/2022.08.27.505439v1](url) to the 16 paediatric GBM samples, validated by a combination of cell clustering methods and differential gene expression analysis.
### What software will you require?
Python 3.9 with packages including scanpy, celltypist, gseapy, scipy, matplotlib, seaborn, numpy, pandas
### What will your first pull request contain?
The analysis module skeleton created by running create-analysis-module.py and initial documentation in the README.md file
### What computational resources will you require?
Only a simplified version of CellTypist, or pre-made models, can be used on a laptop, so this analysis will use AWS to generate a CellTypist model based on GBMap and do the label transfer, in order to use the most accurate form of CellTypist.
### If known, when do you expect to file the first pull request?
24/09 or 25/09
Contributor guide
Research direction
Start with Discussion 722 and the project’s create-analysis-module.py script. Create the analysis module skeleton and initial README.md documentation, then review the CellTypist, scanpy, gseapy, scipy, matplotlib, seaborn, numpy, and pandas requirements and the AWS resource plan. Done means the module structure and README are present and the proposed GBM label-transfer workflow is documented.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- aws, matplotlib, numpy, pandas, python
- Domain
- bioinformatics, cloud, data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100