Metabolinks is a Python package that provides a set of tools for high-resolution MS metabolomics data analysis.
Metabolinks aims at providing several tools that streamline most of the metabolomics workflow. These tools were written having ultra-high resolution MS based metabolomics in mind.
Features are a bit scarce right now:
peak list alignment
common metabolomics data-matrix preprocessing, based on pandas and scikit-learn
Stay tuned, and check out the examples folder.
Installing
Metabolinks is distributed on PyPI and can be installed with pip on a Python 3.8+ installation:
pip install metabolinks
However, it is recommended to install the the scientific Python packages that are required by Metabolinks before using pip. These are listed below, but they can be easily obtained by installing one of the “Scientific/Data Science Python” distributions. One of these two products is highly recommended:
Anaconda Individual Edition (or Miniconda followed by the necessary conda install’s)
Enthought Deployment Manager (followed by the creation of suitable Python environments)
The formal requirements are:
Python 3.8 or above
setuptools, pip, requests, six, pandas-flavor and pytest
and, from the Python scientific ecossystem:
numpy, scipy, matplotlib, pandas and scikit-learn
The installation of the Jupyter platform is also recommended since the examples are provided as Jupyter notebooks.
Metadata
Release files for metabolinks 0.80
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| metabolinks-0.80-py3-none-any.whl | Python 3 | none | any | Details |
Release files / metabolinks-0.80-py3-none-any.whl
| Download URL | metabolinks-0.80-py3-none-any.whl |
|---|---|
| Size | 129.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b8e2d33003fe9fd0509259b429df90c81ed0d4e2ff48033f0667ab1e2f17944f
|
|
BLAKE2b-256 checksum How to use checksums |
aff80b9a3086f440ccd3906d738ebb061b00b1d9a56b3eff760940c1129ec47a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.12
|