combat
pyComBat is a Python 3 implementation of ComBat, one of the most widely used tool for correcting technical biases, called batch effects, in microarray expression data.
Science Score: 26.0%
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Found 1 DOI reference(s) in README -
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○Scientific vocabulary similarity
Low similarity (12.8%) to scientific vocabulary
Repository
pyComBat is a Python 3 implementation of ComBat, one of the most widely used tool for correcting technical biases, called batch effects, in microarray expression data.
Basic Info
- Host: GitHub
- Owner: epigenelabs
- License: gpl-3.0
- Language: Python
- Default Branch: master
- Size: 29.2 MB
Statistics
- Stars: 87
- Watchers: 5
- Forks: 24
- Open Issues: 10
- Releases: 0
Metadata Files
README.md
MIGRATION WARNING
pyComBat has been merged into InMoose, and is no longer maintained as a standalone package. This repository remains up for reference, but will no longer be updated.
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.
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 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
Owner
- Name: Epigene Labs
- Login: epigenelabs
- Kind: organization
- Location: Paris
- Website: www.epigenelabs.com
- Repositories: 3
- Profile: https://github.com/epigenelabs
GitHub Events
Total
- Watch event: 9
- Fork event: 2
Last Year
- Watch event: 9
- Fork event: 2
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Abdelkader Behdenna | a****r@e****m | 48 |
| Aryo Gema | 5****e | 6 |
| abdelkaderbehdenna | 5****a | 4 |
| Maximilien Colange | m****n@e****m | 4 |
| Guillaume Appé | g****e@e****m | 3 |
| Guillaume Appé | g****e@m****e | 3 |
| sergiigladchuk | S****k | 3 |
| aryo | a****o@e****m | 2 |
| Gian Arauz | g****z@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 17
- Total pull requests: 21
- Average time to close issues: 4 months
- Average time to close pull requests: 10 days
- Total issue authors: 16
- Total pull request authors: 9
- Average comments per issue: 1.82
- Average comments per pull request: 0.1
- Merged pull requests: 16
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- such3r (2)
- coldfire79 (1)
- sama2689 (1)
- gokceneraslan (1)
- aliechoes (1)
- tadahayamiz (1)
- JBinkowski (1)
- PARODBE (1)
- haizi-zh (1)
- julienrichardalbert (1)
- aryoepigene (1)
- fredsamhaak (1)
- julienhaziza (1)
- hhageBA (1)
- soleneweill (1)
Pull Request Authors
- aryoepigene (6)
- EpigeneMax (4)
- guillaumeap (3)
- abdelkaderbehdenna (3)
- EstrellaXD (2)
- antoinegaston (1)
- sergiigladchuk (1)
- coldfire79 (1)
- GianArauz (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
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Total downloads:
- pypi 1,636 last-month
- Total docker downloads: 31
-
Total dependent packages: 4
(may contain duplicates) -
Total dependent repositories: 7
(may contain duplicates) - Total versions: 8
- Total maintainers: 1
pypi.org: combat
pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods
- Homepage: https://github.com/epigenelabs/pyComBat
- Documentation: https://combat.readthedocs.io/
- License: GNU General Public License v3 or later (GPLv3+)
-
Latest release: 0.3.3
published over 3 years ago
Rankings
Maintainers (1)
pypi.org: pycombat-test
pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods
- Homepage: https://github.com/epigenelabs/pyComBat
- Documentation: https://pycombat-test.readthedocs.io/
- License: GNU General Public License v3 or later (GPLv3+)
-
Latest release: 0.1.2
published about 6 years ago
Rankings
Maintainers (1)
Dependencies
- mpmath >=1.1.0
- numpy >=1.18.5,<=1.19.5
- pandas >=0.24.2,<=1.1.5
- patsy ==0.5.1