PICor 0.4.0

Isotope correction for MS data

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:

$ pip install picor

You need to have docopt, pandas and scipy installed.

Usage

You can use PICor in two ways:

Command Line

After the installation you can use PICor anywhere from the command line with picor.

picor tests/test_dataset.xlsx NAD -x "dummy column int" -x "dummy column str"

Files with .csv or .xlsx suffix can be used as input files. You can choose the output file (in .csvformat) with the -ooption.picor -h`shows all options.

Python Module

After importing PICor and loading your data (for example a csv file) with pandas you the correction works with:

import pandas as pd
import picor

raw_data = pd.DataFrame(
    {
        "No label": {0: 100, 1: 200, 2: 300, 3: 400, 4: 500, 5: 600},
        "1C13": {0: 100, 1: 100, 2: 100, 3: 100, 4: 100, 5: 100},
        "4C13 6H02 3N15": {0: 30, 1: 40, 2: 50, 3: 60, 4: 70, 5: 80},
        "dummy column str": {0: "C", 1: "ER", 2: "C", 3: "ER", 4: "C", 5: "ER"},
    }
)
corr_data = picor.calc_isotopologue_correction(
    raw_data,
    "NAD",
    exclude_col=["dummy column str"],
)
print(corr_data)

In case the DataFrame contains columns (except the index colum) with other data than raw measurements, you can use either the subset with a list of all columns to be used or exclude_col with a list of the column to be skipped.

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