Batch effects removal for microbiome data via conditional quantile regression on Python
Project description
ConQuR
Batch effects removal for microbiome data via conditional quantile regression on Python.
Free software: MIT license
Documentation: https://conqur.readthedocs.io.
Description
We implement in Python the batch effect removal algorithm described in Ling_et_al.
Implementation used in the paper is written in R. This implementation attempts to be as close as possible, but has some new features (see Features for details).
Features
This implementation supports
using non-negative float features, batch and covariate variables;
using logistic regression with an L2 penalty;
using arbitrary quantiles in quantile regression.
Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
History
1.0.0 (2022-08-03)
First release on PyPI.
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