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        "description": "# DeepTCR\n\n## Deep Learning Methods for Parsing T-Cell Receptor Sequencing (TCRSeq) Data\n\nDeepTCR is a python package that has a collection of unsupervised and supervised \ndeep learning methods to parse TCRSeq data. To see examples of how the algorithms can \nbe used on an example datasets, see the subdirectory 'tutorials' for a collection of tutorial \nuse cases across multiple datasets. For complete documentation for all available methods,\nsee 'Documentation.txt'.\n\nWhile DeepTCR will run with Tensorflow-CPU versions, for optimal training times, \nwe suggest training these algorithms on GPU's (requiring CUDA, cuDNN, and tensorflow-GPU). \n\nDeepTCR now has the added functionality of being able to analyze paired alpha/beta chain inputs as well\nas also being able to take in v/d/j gene usage and the contextual HLA information the TCR-Sequences\nwere seen in (i.e. HLA alleles for a repertoire from a given human sample). For detailed instructions on \nhow to upload this type of data, refer to the documentation for loading data into DeepTCR.  \n\nFor questions or help, email: jsidhom1@jhmi.edu\n\n## Publication\n\nFor full description of algorithm and methods behind DeepTCR, refer to the following manuscript:\n\nSidhom, 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.\n\nhttps://www.biorxiv.org/content/early/2018/11/26/464107\n## Dependencies\n\nDeepTCR has the following python library dependencies:\n1. numpy==1.16.2\n2. pandas==0.23.1\n3. tensorflow==1.13.1\n4. scikit-learn==0.20.3\n5. pickleshare==0.7.4\n6. matplotlib==2.2.2\n7. scipy==1.2.1\n8. biopython==1.73\n9. seaborn==0.9.0\n10. PhenoGraph==1.5.2\n11. umap-learn==0.3.8\n\n## Installation\n\n\nIn order to install DeepTCR:\n\n```python\npip3 install DeepTCR\n\n```\n\nOr to install latest updated versions from Github repo:\n \nEither download package, unzip, and run setup script:\n\n```python\npython3 setup.py install\n```\n\nOr use:\n\n```python\npip3 install git+https://github.com/sidhomj/DeepTCR.git\n\n```\n\n## Release History\n\n### 1.1\nInitial release including two methods for unsupervised learning (VAE & GAN). Also included\nability to handle paired alpha/beta data.\n\n### 1.2\nSecond release included major refactoring in code to streamline and share methods across \nclasses. Included ability for algorithm to accept v/d/j gene usage. Added more analytical fetures and\nvisualization methods. Removed GAN from unsupervised learning techniques. \n\n#### 1.2.7\nOn-graph clustering method introduced for repertoire classifier to improve classification performance.\n\n#### 1.2.13\nAbility for HLA information to be incorporated in the analysis of TCR-Seq. \n\n#### 1.2.24\nAdded ability to do regression for sequence-based model.\n\n### 1.3\nThird release including improved repertoire classification architecture. Details in method will follow \nin manuscript.",
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