maca-python
MACA: Marker-based automatic cell-type annotation for single cell expression data
Science Score: 67.0%
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Repository
MACA: Marker-based automatic cell-type annotation for single cell expression data
Basic Info
Statistics
- Stars: 25
- Watchers: 3
- Forks: 10
- Open Issues: 2
- Releases: 0
Metadata Files
README.md
MACA
MACA: Marker-based automatic cell-type annotation for single cell expression data
1. Installment
MACA works anndata format and is compatible with pipeline analysis through scanpy
pip install scanpy==1.6.0, anndata==0.7.5, scikit-learn
pip install MACA-Python
2. Tutorial for basic use of MACA
See /Tutorial/Basic use of MACA/
MACA_tutorial.ipynb
3. Tutorial for integrated annotation
See /Tutorial/Integrated annotation via MACA/
MACA_integrated_annotation_humanheart.ipynb
MACA_integrated_annotation_humanpancreas.ipynb
MACA_integrated_annotation_humanPBMC.ipynb
4. Standardization of cell type annotation across COVID19 datasets via MACA
See /Tutorial/Integrated annotation via MACA/
MACA_integrated_annotation_COVID19.ipynb
5. Cell-type annotation for 10X Visium data
See /Tutorial/
MACA_transfer_annotation_spatialbrain10xVisium.ipynb
6. Citation
Xu et al. "MACA: marker-based automatic cell-type annotation for single-cell expression data". Bioinformatics
Update 03/12/2021
MACA was modified for parallel computing. For combined ~647K single nuclei human heart data (Tucker et al, Circulation 2020 and Litviňuková et al, Nature 2020), annotation through MACA takes 24 mins with NMI as 0.739 and ARI as 0.818 against authors' annotations.
Update 11/14/2021
We established a new github repo named MASI (https://github.com/hayatlab/MASI), which combines reference data and MACA for fast label transferring.
Update 03/20/2022
We uploaded ScTypeDB, a combination of PanglaoDB and CellMarker, as cell-type marker database, and tested its performance in cell-type annotation. ScTypeDB is compatible to annotation via MACA.
Ianevski et al. "Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data". Nature Communications
Statement
GPU-supported research has speeded up integrative discoveries across single-cell studies. However, access to a good graphic card for model training is not taken granted, especially in undeveloped and developing countries. Even renting a gpu instance on the cloud is costy for researchers.
We devote to make integrative single-cell analysis accessible for most people, and MACA is a cheap solution to label transferring for large single-cell data. MACA annotates 1 million cells for 40 minutes, on a personal laptop with i7-8550U CPU, 16GB memory, and no GPU support.
Owner
- Name: Yang Xu
- Login: ImXman
- Kind: user
- Location: Boston, MA
- Company: Broad institute
- Repositories: 2
- Profile: https://github.com/ImXman
One day I will become Wolverine by editing my genome. Twitter: @Yang_bio
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Xu
given-names: Yang
orcid: https://orcid.org/0000-0003-4173-4337
title: "MACA: Marker-based automatic cell-type annotation for single cell expression data"
version: 1.0.1
date-released: 2021-03-15
GitHub Events
Total
- Issues event: 1
Last Year
- Issues event: 1
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 189
- Total Committers: 1
- Avg Commits per committer: 189.0
- Development Distribution Score (DDS): 0.0
Top Committers
| Name | Commits | |
|---|---|---|
| Yang Xu | 3****n@u****m | 189 |
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 8
- Total pull requests: 0
- Average time to close issues: 3 days
- Average time to close pull requests: N/A
- Total issue authors: 7
- Total pull request authors: 0
- Average comments per issue: 2.13
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 2
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 2
- Pull request authors: 0
- Average comments per issue: 0.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- wdg118 (2)
- QianChwnLyn (1)
- ViriatoII (1)
- ZebinWen (1)
- QuanlongJiang (1)
- Yuxin-Cui (1)
- Edert (1)
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Packages
- Total packages: 1
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Total downloads:
- pypi 17 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 1
- Total maintainers: 1
pypi.org: maca-python
MACA: Marker-based automatic cell-type annotation for single cell expression data
- Homepage: https://github.com/ImXman/MACA
- Documentation: https://maca-python.readthedocs.io/
- License: GNU General Public License v3 (GPLv3)
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Latest release: 1.0.1
published over 4 years ago