| Name | combat JSON |
| Version |
0.3.3
JSON |
| download |
| home_page | https://github.com/epigenelabs/pyComBat |
| Summary | pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods |
| upload_time | 2022-11-29 15:42:06 |
| maintainer | |
| docs_url | None |
| author | Abdelkader Behdenna |
| requires_python | >=3.6 |
| license | |
| keywords |
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| VCS |
 |
| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# pyComBat
pyComBat [1] is a Python 3 implementation of ComBat [2], one of the most widely used tool for correcting technical biases, called batch effects, in microarray expression data.
More detailed documentation can be found at [this address](https://epigenelabs.github.io/pyComBat/).
## TO DO
## Minimum dependencies
We list here the versions of the packages that have been used for development/testing of pyComBat, as well as for writing the documentation.
### pyComBat dependencies
* python 3.6
* numpy 1.18.5
* mpmath 1.1.0
* pandas 0.24.2
* patsy 0.5.1
### Documentation
* sphinx 2.1.2
## Usage example
### Installation
You can install pyComBat directly with:
```python
pip install combat
```
You can upgrade pyComBat to its latest version with:
```python
pip install combat --upgrade
```
### Running pyComBat
The simplest way of using pyComBat is to first import it, and then simply use the pycombat function with default parameters:
```python
from combat.pycombat import pycombat
data_corrected = pycombat(data,batch)
```
* data: The expression matrix as a dataframe. It contains the information about the gene expression (rows) for each sample (columns).
* batch: List of batch indexes. The batch list describes the batch for each sample. The list of batches contains as many elements as the number of columns in the expression matrix.
## How to contribute
Please refer to [CONTRIBUTING.md](https://github.com/epigenelabs/pyComBat/blob/master/CONTRIBUTING.md) to learn more about the contribution guidelines.
## References
[1] Behdenna A, Haziza J, Azencot CA and Nordor A. (2020) pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. bioRxiv doi: 10.1101/2020.03.17.995431
[2] Johnson W E, et al. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118–127
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"description": "# pyComBat\n\npyComBat [1] is a Python 3 implementation of ComBat [2], one of the most widely used tool for correcting technical biases, called batch effects, in microarray expression data.\n\nMore detailed documentation can be found at [this address](https://epigenelabs.github.io/pyComBat/).\n\n## TO DO\n\n## Minimum dependencies\n\nWe list here the versions of the packages that have been used for development/testing of pyComBat, as well as for writing the documentation.\n\n### pyComBat dependencies\n\n* python 3.6\n\n* numpy 1.18.5\n\n* mpmath 1.1.0\n\n* pandas 0.24.2\n\n* patsy 0.5.1\n\n### Documentation\n\n* sphinx 2.1.2\n\n## Usage example\n\n### Installation\n\nYou can install pyComBat directly with:\n\n```python\npip install combat\n```\n\nYou can upgrade pyComBat to its latest version with:\n\n```python\npip install combat --upgrade\n```\n\n### Running pyComBat\n\nThe simplest way of using pyComBat is to first import it, and then simply use the pycombat function with default parameters:\n\n```python\nfrom combat.pycombat import pycombat\ndata_corrected = pycombat(data,batch)\n```\n\n* data: The expression matrix as a dataframe. It contains the information about the gene expression (rows) for each sample (columns).\n\n* batch: List of batch indexes. The batch list describes the batch for each sample. The list of batches contains as many elements as the number of columns in the expression matrix.\n\n## How to contribute\n\nPlease refer to [CONTRIBUTING.md](https://github.com/epigenelabs/pyComBat/blob/master/CONTRIBUTING.md) to learn more about the contribution guidelines.\n\n## References\n\n[1] Behdenna A, Haziza J, Azencot CA and Nordor A. (2020) pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. bioRxiv doi: 10.1101/2020.03.17.995431\n\n[2] Johnson W E, et al. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118\u2013127\n",
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