Metadata-Version: 2.1
Name: PICor
Version: 0.3.3
Summary: Isotope correction for MS data
Home-page: https://github.com/MolecularBioinformatics/PICor
Author: Jørn Dietze
Author-email: jorn.dietze@uit.no
License: gpl3
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Requires-Python: >=3.6
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Requires-Dist: pandas
Requires-Dist: scipy
Provides-Extra: testing
Requires-Dist: pytest ; extra == 'testing'
Requires-Dist: pytest-cov ; extra == 'testing'

# PICor: Statistical Isotope Correction

ICor is a python package for correcting mass spectrometry data for the effect of natural isotope abundance.


## Description

PICor takes pandas DataFrames of the measured integrated MS intensities as input, corrects them for natural isotope abundance and returns a DataFrame again.

PICor can also correct for overlapping isotopologues due to too low resoltion.For example the 13-C4 and 2-H4 isotopologues of the metabolite NAD can't be resolved at a resolution of 60,000 at 200 m/z.

## Installation

To install:
```bash
$ pip install picor
```

You need to have `pandas` and `scipy` installed.

## Usage

After importing PICor and loading your data (for example a csv file) with pandas you the correction works with:
```python
import pandas as pd
import picor

raw_data = pd.read_csv("data.csv", index="Time in h"))
corr_data = picor.calc_isotopologue_correction(
	raw_data,
	"NAD",
	)
print(corr_data)
```

You can activate a resolution depent correction by setting  `resolution_correction` to `True`. Specify the resolution and the reference m/z ratio with `resolution` and `mz_calibration`.


Jørn Dietze, UiT - The Arctic University of Tromsø, 2020


