Beta MetaTiME: annotate TME scRNA cell states
Project description
MetaTiME: Meta-components in Tumor immune MicroEnvironment
MetaTiME learns data-driven, interpretable, and reproducible gene programs by integrating millions of single cells from hundreds of tumor scRNA-seq data. The idea is to learn a map of single-cell space with biologically meaningful directions from large-scale data, which helps understand functional cell states and transfers knowledge to new data analysis. MetaTiME provides pretrained meta-components (MeCs) to automatically annotate fine-grained cell states and plot signature continuum for new single-cells of tumor microenvironment.
Installation
Create a new virtual env and activate (optional)
python -m venv metatime-env; source metatime-env/bin/activate
Use pip to install
pip install metatime
Installation shall be in minutes .
Next we have a tutorial on applying MetaTiME on new TME scRNAseq data to annotate cell states, scoring signature continuum, and test differential signature activity.
Usage
MetaTiME-Annotator
Interactive tutorial
Use MetaTiME to automatically annotate cell states and map signatures
Method
Reference
Manuscript In Revision. Repo continously being improved! More details will be updated and suggested improvements welcome.
Accepted at Nature Communications [Journal Article doi pending]
Training Datasets
Tumor scRNAseq Data for MetaTiME @ Zenodo
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A large collection of uniformly processed tumor single-cell RNA-seq.
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Includes raw data and MetaTiME score for the TME cells.
Dependency
- pandas
- scanpy
- anndata
- matplotlib
- adjustText
- leidenalg
- harmonypy
Dependency version tested:
- pandas==1.1.5
- scanpy==1.8.2
- anndata==0.8.0
- matplotlib==3.5.1
- adjustText==0.7.3
- leidenalg==0.8.3
Contact
Yi Zhang, Ph.D.
yiz [AT] ds.dfci.harvard.edu Twitter | Website Research Fellow Department of Data Science Dana-Farber Cancer Institute Harvard University T.H. Chan School of Public Health
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