Metadata-Version: 2.1
Name: metabo_adni
Version: 0.5.8
Summary: Metabolomics data processing for the ADNI data sets.
Home-page: https://github.com/tomszar/metabo_adni
License: GNU General Public License v3.0
Keywords: metabolomics,quality control,adni,alzheimer's disease
Author: Tomas Gonzalez Zarzar
Author-email: tomasgzarzar@gmail.com
Requires-Python: >=3.12,<4.0
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: openpyxl (>=3.1,<4.0)
Requires-Dist: pandas (>=2.2,<3.0)
Requires-Dist: pingouin (==0.5.4)
Project-URL: Repository, https://github.com/tomszar/metabo_adni
Description-Content-Type: text/markdown

# Metabo_ADNI
[![PyPI version](https://badge.fury.io/py/metabo_adni.svg)](https://pypi.org/project/metabo-adni/)

Metabolomics data processing for the ADNI data sets.
Currently, only supports the Biocrates p180 and Nightingale NMR platforms.

## Installation

metabo_adni is distributed as a python package, so install it by running:

```bash
pip install metabo_adni
```

## Usage

In the folder with the required datasets, simply run:

```bash
clean_files
```

And metabo_adni will run with the default parameters.
**Note:** do not change the original name of the files.

### Options

- `-D`: define the directory were the files are located. Default, current working directory
- `-P`: define the platform, either p180 or nmr. Default, p180
- `-F`: define the fasting file. Default, BIOMARK.csv
- `-L`: define the directory were the LOD p180 files are located. Default, current working directory
- `--mmc`: remove metabolites with missing proportions greater than cutoff. Default, 0.2
- `--mpc`: remove participants with missing proportions greater than cutoff. Default, 0.2
- `--cv`: remove metabolites with CV values greater than cutoff. Default, 0.2
- `--icc`: remove metabolites with ICC values lower than cutoff. Default, 0.65
- `--log2`: apply log2 transformation to metabolite concentration values
- `--merge`: merge data frames across cohorts
- `--zscore`: apply zscore transformation to metabolite concentration values
- `--winsorize`: winsorize extreme values (more than 3 std of mean)
- `--remove-moutliers`: remove multivariate outliers using the Mahalanobis distance
- `--residualize-meds`: replace metabolite values with residuals from a regression with medication intake. Note that residuals are scaled to unit variance

