Metadata-Version: 2.1
Name: pyserrf
Version: 0.1.1
Summary: Python implementation of the Systematic Error Removal Using Random Forest algorithm
Author: matteobolner
Author-email: matteo.bolner2@unibo.it
Requires-Python: >=3.10,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: numpy (>=1.26.4,<2.0.0)
Requires-Dist: pandas (>=2.2.2,<3.0.0)
Requires-Dist: pytest (>=8.2.0,<9.0.0)
Requires-Dist: scikit-learn (>=1.4.2,<2.0.0)
Requires-Dist: tqdm (>=4.66.4,<5.0.0)
Description-Content-Type: text/markdown

# pySERRF
Python implementation of the Systematic Error Removal Using Random Forest (SERRF) algorithm.
SERRF is a qc-based sample normalization method designed for large-scale untargeted metabolomics data.
The method was developed by the Fan et al. in 2015 (see https://slfan2013.github.io/SERRF-online/).
This is simply an attempt to port its functionality from R to python.
The package structure is based on SKlearn's transformers, with fit and transform methods.

TODO: Implement cross-validation
TODO: Verify if injection time is accounted for with current code
TODO: Add documentation
TODO: Add CLI
