Metadata-Version: 1.0
Name: DeepTCR
Version: 1.2.14
Summary: Deep Learning Methods for Parsing T-Cell Receptor Sequencing (TCRSeq) Data
Home-page: https://github.com/sidhomj/DeepTCR
Author: John-William Sidhom
Author-email: jsidhom1@jhmi.edu
License: LICENSE
Description: # DeepTCR
        
        ## Deep Learning Methods for Parsing T-Cell Receptor Sequencing (TCRSeq) Data
        
        DeepTCR is a python package that has a collection of unsupervised and supervised 
        deep learning methods to parse TCRSeq data. To see examples of how the algorithms can 
        be used on an example datasets, see the subdirectory 'tutorials' for a collection of tutorial 
        use cases across multiple datasets. For complete documentation for all available methods,
        see 'Documentation.txt'.
        
        While DeepTCR will run with Tensorflow-CPU versions, for optimal training times, 
        we suggest training these algorithms on GPU's (requiring CUDA, cuDNN, and tensorflow-GPU). 
        
        DeepTCR now has the added functionality of being able to analyze paired alpha/beta chain inputs as well
        as also being able to take in v/d/j gene usage and the contextual HLA information the TCR-Sequences
        were seen in (i.e. HLA alleles for a repertoire from a given human sample). For detailed instructions on 
        how to upload this type of data, refer to the documentation for loading data into DeepTCR.  
        
        For questions or help, email: jsidhom1@jhmi.edu
        
        ## Publication
        
        For full description of algorithm and methods behind DeepTCR, refer to the following manuscript:
        
        Sidhom, J. W., Larman, H. B., Pardoll, D. M., & Baras, A. S. (2018). DeepTCR: a deep learning framework for revealing structural concepts within TCR Repertoire. bioRxiv, 464107.
        
        https://www.biorxiv.org/content/early/2018/11/26/464107
        ## Dependencies
        
        DeepTCR has the following python library dependencies:
        1. numpy==1.14.5
        2. pandas==0.23.1
        3. tensorflow==1.11.0
        4. scikit-learn==0.19.1
        5. pickleshare==0.7.4
        6. matplotlib==2.2.2
        7. scipy==1.1.0
        8. biopython==1.69
        9. seaborn==0.9.0
        10. PhenoGraph==1.5.2
        
        
        ## Installation
        
        
        In order to install DeepTCR:
        
        ```python
        pip3 install DeepTCR
        
        ```
        
        Or to install latest updated versions from Github repo:
         
        Either download package, unzip, and run setup script:
        
        ```python
        python3 setup.py install
        ```
        
        Or use:
        
        ```python
        pip3 install git+https://github.com/sidhomj/DeepTCR.git
        
        ```
        
        ## Release History
        
        ### 1.1
        Initial release including two methods for unsupervised learning (VAE & GAN). Also included
        ability to handle paired alpha/beta data.
        
        ### 1.2
        Second release included major refactoring in code to streamline and share methods across 
        classes. Included ability for algorithm to accept v/d/j gene usage. Added more analytical fetures and
        visualization methods. Removed GAN from unsupervised learning techniques. 
        
        #### 1.2.7
        On-graph clustering method introduced for repertoire classifier to improve classification performance.
        
        #### 1.2.13
        Ability for HLA information to be incorporated in the analysis of TCR-Seq. 
        
        
        
        
        
        
Platform: UNKNOWN
