| Name | njab JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | not Just Another Biomarker |
| upload_time | 2024-10-16 12:31:33 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2022 University of Copenhagen Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
bioinformatics
biomarker
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# (not) Just Another Biomarker (nJAB)
`njab` is a collection of some python function building on top of
`pandas`, `scikit-learn`, `statsmodels`, `pingoin`, `numpy` and more...
It aims to formalize a procedure for biomarker discovery which was first developed for
a paper on alcohol-related liver disease, based on mass spectrometry-based proteomics
measurements of blood plasma samples:
> Niu, L., Thiele, M., Geyer, P. E., Rasmussen, D. N., Webel, H. E.,
> Santos, A., Gupta, R., Meier, F., Strauss, M., Kjaergaard, M., Lindvig,
> K., Jacobsen, S., Rasmussen, S., Hansen, T., Krag, A., & Mann, M. (2022).
> “Noninvasive Proteomic Biomarkers for Alcohol-Related Liver Disease.”
> Nature Medicine 28 (6): 1277–87.
> [nature.com/articles/s41591-022-01850-y](https://www.nature.com/articles/s41591-022-01850-y)
The approach was formalized for an analysis of inflammation markers of a cohort of patients with alcohol related cirrhosis,
based on OLink-based proteomics measurments of blood plasma samples:
> Mynster Kronborg, T., Webel, H., O’Connell, M. B., Danielsen, K. V., Hobolth, L., Møller, S., Jensen, R. T., Bendtsen, F., Hansen, T., Rasmussen, S., Juel, H. B., & Kimer, N. (2023).
> Markers of inflammation predict survival in newly diagnosed cirrhosis: a prospective registry study.
> Scientific Reports, 13(1), 1–11.
> [nature.com/articles/s41598-023-47384-2](https://www.nature.com/articles/s41598-023-47384-2)
## Installation
Install using pip from [PyPi](https://pypi.org/project/njab) version.
```
pip install njab
```
or directly from github
```
pip install git+https://github.com/RasmussenLab/njab.git
```
## Tutorials
The tutorial can be found on the documentation of the project with output
or can be run directly in colab.
### Explorative Analysis of survival dataset
[](https://colab.research.google.com/github/RasmussenLab/njab/blob/HEAD/docs/tutorial/explorative_analysis.ipynb)
The tutorial builds on a dataset example of survival of prostatic cancer.
The main steps in the tutorial are:
1. Data loading and inspection
2. Uncontrolled binary and t-tests for binary and continous variables respectively
3. ANCOVA analysis controlling for age and weight, corrected for multiple testing
4. Kaplan-Meier plots of for significant features
### Biomarker discovery tutrial
[](https://colab.research.google.com/github/RasmussenLab/njab/blob/HEAD/docs/tutorial/log_reg.ipynb)
All steps are describe in the tutorial, where you could load your own data with minor adaptions.
The tutorial build on an curated [Alzheimer dataset from omiclearn](https://github.com/MannLabs/OmicLearn/tree/master/omiclearn/data). See the [Alzheimer Data](https://github.com/RasmussenLab/njab/tree/HEAD/docs/tutorial/data) section for more information.
The main steps in the tutorial are:
1. Load and prepare data for machine learning
2. Find a good set of features using cross validation
3. Evaluate and inspect your model retrained on the entire training data
## Documentation
Please find the documentation under [njab.readthedocs.io](https://njab.readthedocs.io)
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"description": "# (not) Just Another Biomarker (nJAB)\n\n`njab` is a collection of some python function building on top of \n`pandas`, `scikit-learn`, `statsmodels`, `pingoin`, `numpy` and more...\n\nIt aims to formalize a procedure for biomarker discovery which was first developed for \na paper on alcohol-related liver disease, based on mass spectrometry-based proteomics\nmeasurements of blood plasma samples:\n\n> Niu, L., Thiele, M., Geyer, P. E., Rasmussen, D. N., Webel, H. E., \n> Santos, A., Gupta, R., Meier, F., Strauss, M., Kjaergaard, M., Lindvig, \n> K., Jacobsen, S., Rasmussen, S., Hansen, T., Krag, A., & Mann, M. (2022). \n> \u201cNoninvasive Proteomic Biomarkers for Alcohol-Related Liver Disease.\u201d \n> Nature Medicine 28 (6): 1277\u201387. \n> [nature.com/articles/s41591-022-01850-y](https://www.nature.com/articles/s41591-022-01850-y)\n\nThe approach was formalized for an analysis of inflammation markers of a cohort of patients with alcohol related cirrhosis, \nbased on OLink-based proteomics measurments of blood plasma samples:\n> Mynster Kronborg, T., Webel, H., O\u2019Connell, M. B., Danielsen, K. V., Hobolth, L., M\u00f8ller, S., Jensen, R. T., Bendtsen, F., Hansen, T., Rasmussen, S., Juel, H. B., & Kimer, N. (2023). \n> Markers of inflammation predict survival in newly diagnosed cirrhosis: a prospective registry study. \n> Scientific Reports, 13(1), 1\u201311. \n> [nature.com/articles/s41598-023-47384-2](https://www.nature.com/articles/s41598-023-47384-2)\n\n## Installation\n\nInstall using pip from [PyPi](https://pypi.org/project/njab) version.\n\n```\npip install njab\n```\n\nor directly from github\n\n```\npip install git+https://github.com/RasmussenLab/njab.git\n```\n\n## Tutorials\n\nThe tutorial can be found on the documentation of the project with output\nor can be run directly in colab.\n\n### Explorative Analysis of survival dataset\n\n[](https://colab.research.google.com/github/RasmussenLab/njab/blob/HEAD/docs/tutorial/explorative_analysis.ipynb)\n\nThe tutorial builds on a dataset example of survival of prostatic cancer.\n\nThe main steps in the tutorial are:\n\n1. Data loading and inspection\n2. Uncontrolled binary and t-tests for binary and continous variables respectively\n3. ANCOVA analysis controlling for age and weight, corrected for multiple testing\n4. Kaplan-Meier plots of for significant features\n\n### Biomarker discovery tutrial \n\n[](https://colab.research.google.com/github/RasmussenLab/njab/blob/HEAD/docs/tutorial/log_reg.ipynb)\n\n\nAll steps are describe in the tutorial, where you could load your own data with minor adaptions.\nThe tutorial build on an curated [Alzheimer dataset from omiclearn](https://github.com/MannLabs/OmicLearn/tree/master/omiclearn/data). See the [Alzheimer Data](https://github.com/RasmussenLab/njab/tree/HEAD/docs/tutorial/data) section for more information.\n\nThe main steps in the tutorial are:\n\n1. Load and prepare data for machine learning\n2. Find a good set of features using cross validation\n3. Evaluate and inspect your model retrained on the entire training data\n\n## Documentation\n\nPlease find the documentation under [njab.readthedocs.io](https://njab.readthedocs.io)\n",
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