tidytcells


Nametidytcells JSON
Version 2.1.4 PyPI version JSON
download
home_pageNone
SummaryStandardise TR/MH data
upload_time2025-01-12 11:33:35
maintainerNone
docs_urlNone
authorNone
requires_python>=3.8
licenseNone
keywords immunology bioinformatics tcr tr mhc mh hla t cell imgt
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            <div align="center">

<img src="https://raw.githubusercontent.com/yutanagano/tidytcells/main/tidytcells.png" width=700>
<br><br>

![Tests](https://github.com/yutanagano/tidytcells/actions/workflows/tests.yaml/badge.svg)
[![Docs](https://readthedocs.org/projects/tidytcells/badge/?version=latest)](https://tidytcells.readthedocs.io)
[![License](https://img.shields.io/badge/license-MIT-blue)](https://github.com/yutanagano/tidytcells?tab=MIT-1-ov-file#readme)
[![DOI](https://img.shields.io/badge/DOI-10.3389/fimmu.2023.1276106-pink)](https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2023.1276106)

### Check out the [documentation page](https://tidytcells.readthedocs.io).

</div>

---

`tidytcells` is a lightweight python package that cleans and standardizes T cell receptor (TR) and Major Histocompatibility (MH) data to be [IMGT](https://www.imgt.org/)-compliant.
The main purpose of the package is to solve the problem of parsing and collating together non-standardized TR datasets.
It is often difficult to compile TR data from multiple sources because the formats/nomenclature of how each dataset encodes TR and MH gene names are slightly different, or even inconsistent within themselves.
`tidytcells` can ameliorate this issue by auto-correcting and auto-standardizing your data.

## Installation

```bash
$ pip install tidytcells
```

## Citing tidytcells

Please cite [our manuscript](https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2023.1276106).

### BibTex
```bibtex
@ARTICLE{10.3389/fimmu.2023.1276106,
         AUTHOR={Nagano, Yuta  and Chain, Benjamin },
         TITLE={tidytcells: standardizer for TR/MH nomenclature},
         JOURNAL={Frontiers in Immunology},
         VOLUME={14},
         YEAR={2023},
         URL={https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2023.1276106},
         DOI={10.3389/fimmu.2023.1276106},
         ISSN={1664-3224}
}
```

            

Raw data

            {
    "_id": null,
    "home_page": null,
    "name": "tidytcells",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.8",
    "maintainer_email": "Yuta Nagano <yutanagano51@proton.me>",
    "keywords": "immunology, bioinformatics, TCR, TR, MHC, MH, HLA, T cell, IMGT",
    "author": null,
    "author_email": "Yuta Nagano <yutanagano51@proton.me>",
    "download_url": "https://files.pythonhosted.org/packages/21/c7/4c8b1934d80b636c27f0913ccab905269ca00b3860a2481582e83d723d6d/tidytcells-2.1.4.tar.gz",
    "platform": null,
    "description": "<div align=\"center\">\n\n<img src=\"https://raw.githubusercontent.com/yutanagano/tidytcells/main/tidytcells.png\" width=700>\n<br><br>\n\n![Tests](https://github.com/yutanagano/tidytcells/actions/workflows/tests.yaml/badge.svg)\n[![Docs](https://readthedocs.org/projects/tidytcells/badge/?version=latest)](https://tidytcells.readthedocs.io)\n[![License](https://img.shields.io/badge/license-MIT-blue)](https://github.com/yutanagano/tidytcells?tab=MIT-1-ov-file#readme)\n[![DOI](https://img.shields.io/badge/DOI-10.3389/fimmu.2023.1276106-pink)](https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2023.1276106)\n\n### Check out the [documentation page](https://tidytcells.readthedocs.io).\n\n</div>\n\n---\n\n`tidytcells` is a lightweight python package that cleans and standardizes T cell receptor (TR) and Major Histocompatibility (MH) data to be [IMGT](https://www.imgt.org/)-compliant.\nThe main purpose of the package is to solve the problem of parsing and collating together non-standardized TR datasets.\nIt is often difficult to compile TR data from multiple sources because the formats/nomenclature of how each dataset encodes TR and MH gene names are slightly different, or even inconsistent within themselves.\n`tidytcells` can ameliorate this issue by auto-correcting and auto-standardizing your data.\n\n## Installation\n\n```bash\n$ pip install tidytcells\n```\n\n## Citing tidytcells\n\nPlease cite [our manuscript](https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2023.1276106).\n\n### BibTex\n```bibtex\n@ARTICLE{10.3389/fimmu.2023.1276106,\n         AUTHOR={Nagano, Yuta  and Chain, Benjamin },\n         TITLE={tidytcells: standardizer for TR/MH nomenclature},\n         JOURNAL={Frontiers in Immunology},\n         VOLUME={14},\n         YEAR={2023},\n         URL={https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2023.1276106},\n         DOI={10.3389/fimmu.2023.1276106},\n         ISSN={1664-3224}\n}\n```\n",
    "bugtrack_url": null,
    "license": null,
    "summary": "Standardise TR/MH data",
    "version": "2.1.4",
    "project_urls": {
        "Documentation": "https://tidytcells.readthedocs.io",
        "Homepage": "https://tidytcells.readthedocs.io",
        "Issues": "https://github.com/yutanagano/tidytcells/issues",
        "Repository": "https://github.com/yutanagano/tidytcells"
    },
    "split_keywords": [
        "immunology",
        " bioinformatics",
        " tcr",
        " tr",
        " mhc",
        " mh",
        " hla",
        " t cell",
        " imgt"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "89757b1b8b68f409adc2b4bf403432f1561f695d37877f3a0c3128cb8d866d71",
                "md5": "afea409aa4068f4942e091f9402a6c25",
                "sha256": "4b5d8c0ec76dcaa273d482966c9248ce1fb7f4b1217380fdd4c2392ef9391c13"
            },
            "downloads": -1,
            "filename": "tidytcells-2.1.4-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "afea409aa4068f4942e091f9402a6c25",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.8",
            "size": 93470,
            "upload_time": "2025-01-12T11:33:32",
            "upload_time_iso_8601": "2025-01-12T11:33:32.415324Z",
            "url": "https://files.pythonhosted.org/packages/89/75/7b1b8b68f409adc2b4bf403432f1561f695d37877f3a0c3128cb8d866d71/tidytcells-2.1.4-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "21c74c8b1934d80b636c27f0913ccab905269ca00b3860a2481582e83d723d6d",
                "md5": "b80468c9bba13a964bc820718ab54932",
                "sha256": "3724988d07d724d1a464913f37816fbcdf64f1cd7e11c93891f43e116bdb1d12"
            },
            "downloads": -1,
            "filename": "tidytcells-2.1.4.tar.gz",
            "has_sig": false,
            "md5_digest": "b80468c9bba13a964bc820718ab54932",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.8",
            "size": 81060,
            "upload_time": "2025-01-12T11:33:35",
            "upload_time_iso_8601": "2025-01-12T11:33:35.109567Z",
            "url": "https://files.pythonhosted.org/packages/21/c7/4c8b1934d80b636c27f0913ccab905269ca00b3860a2481582e83d723d6d/tidytcells-2.1.4.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2025-01-12 11:33:35",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "yutanagano",
    "github_project": "tidytcells",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "tox": true,
    "lcname": "tidytcells"
}
        
Elapsed time: 0.44892s