# Metabo_ADNI
[](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
Raw data
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"description": "# Metabo_ADNI\n[](https://pypi.org/project/metabo-adni/)\n\nMetabolomics data processing for the ADNI data sets.\nCurrently, only supports the Biocrates p180 and Nightingale NMR platforms.\n\n## Installation\n\nmetabo_adni is distributed as a python package, so install it by running:\n\n```bash\npip install metabo_adni\n```\n\n## Usage\n\nIn the folder with the required datasets, simply run:\n\n```bash\nclean_files\n```\n\nAnd metabo_adni will run with the default parameters.\n**Note:** do not change the original name of the files.\n\n### Options\n\n- `-D`: define the directory were the files are located. Default, current working directory\n- `-P`: define the platform, either p180 or nmr. Default, p180\n- `-F`: define the fasting file. Default, BIOMARK.csv\n- `-L`: define the directory were the LOD p180 files are located. Default, current working directory\n- `--mmc`: remove metabolites with missing proportions greater than cutoff. Default, 0.2\n- `--mpc`: remove participants with missing proportions greater than cutoff. Default, 0.2\n- `--cv`: remove metabolites with CV values greater than cutoff. Default, 0.2\n- `--icc`: remove metabolites with ICC values lower than cutoff. Default, 0.65\n- `--log2`: apply log2 transformation to metabolite concentration values\n- `--merge`: merge data frames across cohorts\n- `--zscore`: apply zscore transformation to metabolite concentration values\n- `--winsorize`: winsorize extreme values (more than 3 std of mean)\n- `--remove-moutliers`: remove multivariate outliers using the Mahalanobis distance\n- `--residualize-meds`: replace metabolite values with residuals from a regression with medication intake. Note that residuals are scaled to unit variance\n",
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