Metadata-Version: 2.1
Name: metatime
Version: 1.3.0
Summary: Beta MetaTiME: annotate TME scRNA cell states
Home-page: https://github.com/yi-zhang/MetaTiME.git
Author: Yi Zhang
Author-email: wingsyiz@gmail.com
License: UNKNOWN
Description: # MetaTiME: Meta-components in Tumor immune MicroEnvironment 
        <p align="left"><img src="https://raw.githubusercontent.com/yi-zhang/MetaTiME/main/docs/source/_static/img/logo.png" width="290" height="240"></p>
        
        [![PyPI](https://img.shields.io/pypi/v/metatime.svg)](https://pypi.org/project/metatime/)
        [![Documentation Status](https://readthedocs.org/projects/metatime/badge/?version=latest)](https://metatime.readthedocs.io/en/latest/?badge=latest)
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7410180.svg)](https://doi.org/10.5281/zenodo.7410180)
        
        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
         - [Use MetaTiME to automatically annotate cell states and map signatures](https://github.com/yi-zhang/MetaTiME/blob/main/docs/notebooks/metatime_annotator.ipynb)
        
        ### Interactive tutorial
        [Use MetaTiME to automatically annotate cell states and map signatures ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yi-zhang/MetaTiME/blob/main/docs/notebooks/metatime_annotator.ipynb)
        
        ## Method 
        <p align="left"><img src="https://raw.githubusercontent.com/yi-zhang/MetaTiME/main/docs/source/_static/img/fig1.png" width="700" height="400"></p>
         
        
        ## Reference
        Manuscript In Revision. Repo continously being improved! More details will be updated and suggested improvements welcome. 
        
        [Paper at bioRxiv](https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1)
        
        Accepted at Nature Communications [Journal Article doi pending]
        
        ## Training Datasets
        
        Tumor scRNAseq Data for MetaTiME @ [Zenodo](https://zenodo.org/record/7410180)
        
        - A large collection of uniformly processed tumor single-cell RNA-seq. 
        
        - 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](https://twitter.com/Wings7Spread) |  [Website](https://yi-zhang.github.io/)
        Research Fellow
        Department of Data Science
        Dana-Farber Cancer Institute
        Harvard University T.H. Chan School of Public Health
        
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
