Metadata-Version: 2.1
Name: tcrdist3
Version: 0.1.6
Summary: flexible distance measures for comparing T cell receptors
Home-page: https://github.com/kmayerb/tcrdist3
Author: Koshlan Mayer-Blackwell
Author-email: kmayerbl@fredhutch.org
Maintainer: Koshlan Mayer-Blackwell
Maintainer-email: kmayerbl@fredhutch.org
License: MIT
Description: # tcrdist3
        
        ![Python application](https://github.com/kmayerb/tcrdist3/workflows/Python%20application/badge.svg?event=push) [![Coverage Status](https://coveralls.io/repos/github/kmayerb/tcrdist3/badge.svg?branch=master)](https://coveralls.io/github/kmayerb/tcrdist3?branch=master)[![Documentation Status](https://readthedocs.org/projects/tcrdist3/badge/?version=latest)](https://tcrdist3.readthedocs.io/en/latest/?badge=latest)
        [![Docker Repository on Quay](https://quay.io/repository/kmayerb/tcrdist3/status "Docker Repository on Quay")](https://quay.io/repository/kmayerb/tcrdist3)
        
        Flexible distance measures for comparing T cell receptors 
        
        tcrdist3 is a python API-enabled toolkit for analyzing T-cell receptor repertoires. Some of the functionality and code is adapted from the original tcr-dist package which was released with the publication of Dash et al. Nature (2017) doi:10.1038/nature22383. This package contains a new API for computing tcrdistance measures as well as new features.
        
        
        ## Installation
        
        ```
        pip install git+https://github.com/kmayerb/tcrdist3.git@0.1.6
        ```
        
        ## Docker
        [![Docker Repository on Quay](https://quay.io/repository/kmayerb/tcrdist3/status "Docker Repository on Quay")](https://quay.io/repository/kmayerb/tcrdist3)
        
        ```
        docker pull quay.io/kmayerb/tcrdist3:0.1.6
        ```
        
        ## Documentation
        [![Documentation Status](https://readthedocs.org/projects/tcrdist3/badge/?version=latest)](https://tcrdist3.readthedocs.io/en/latest/?badge=latest)
        
        More documentation can be found at [tcrdist3.readthedocs](https://tcrdist3.readthedocs.io/).
        
        ## Basic Usage
        
        ```python
        import pandas as pd
        from tcrdist.repertoire import TCRrep
        
        df = pd.read_csv("dash.csv")
        tr = TCRrep(cell_df = df, 
                    organism = 'mouse', 
                    chains = ['alpha','beta'], 
                    db_file = 'alphabeta_gammadelta_db.tsv')
        
        tr.pw_alpha
        tr.pw_beta
        tr.pw_cdr3_a_aa
        tr.pw_cdr3_b_aa
        ```
        
        ## Citing
        
        ##### Quantifiable predictive features define epitope-specific T cell receptor repertoires
        
        Pradyot Dash, Andrew J. Fiore-Gartland, Tomer Hertz, George C. Wang, Shalini Sharma, Aisha Souquette, Jeremy Chase Crawford, E. Bridie Clemens, Thi H. O. Nguyen, Katherine Kedzierska, Nicole L. La Gruta, Philip Bradley & Paul G. Thomas [Nature (2017)](https://doi.org/10.1038/nature22383).
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
