PeakPerformance is a Python toolbox for estimating peak areas from mass-spectrometry or HPLC datasets under uncertainty. It applies Bayesian inference using the PyMC library to parametric models of peak curves.

405 commits | Last commit 1 week ago

Cite this software

What PeakPerformance can do for you

PyPI version pipeline coverage documentation DOI

How to use PeakPerformance

For installation instructions, see For instructions regarding the use of PeakPerformance, check out the example notebook(s) under notebooks, the complementary example data under example, and the following introductory explanations.

Preparing raw data

This step is crucial when using PeakPerformance. Raw data has to be supplied as time series meaning for each signal you want to analyze, save a NumPy array consisting of time in the first dimension and intensity in the second dimension (compare example data). Both time and intensity should also be NumPy arrays. If you e.g. have time and intensity of a singal as lists, you can use the following code to convert, format, and save them in the correct manner:

import numpy as np
from pathlib import Path

time_series = np.array([np.array(time), np.array(intensity)])"example_path/time_series.npy"), time_series)

The naming convention of raw data files is <acquisition name>_<precursor ion m/z or experiment number>_<product ion m/z start>_<product ion m/z end>.npy. There should be no underscores within the named sections such as acquisition name. Essentially, the raw data names include the acquisition and mass trace, thus yielding a recognizable and unique name for each isotopomer/fragment/metabolite/sample.

Model selection

When it comes to selecting models, PeakPerformance has a function performing an automated selection process by analyzing one acquisiton per mass trace with all implemented models. Subsequently, all models are ranked based on an information criterion (either pareto-smoothed importance sampling leave-one-out cross-validation or widely applicable information criterion). For this process to work as intended, you need to specify acquisitions with representative peaks for each mass trace (see example notebook 1). If e.g. most peaks of an analyte show a skewed shape, then select an acquisition where this is the case. For double peaks, select an acquision where the peaks are as distinct and comparable in height as possible. Since model selection is a computationally demanding and time consuming process, it is suggested to state the model type as the user (see example notebook 1) if possible.


A batch run broke and I want to restart it.

If an error occured in the middle of a batch run, then you can use the pipeline_restart function in the pipeline module to create a new batch which will analyze only those samples, which have not been analyzed previously.

The model parameters don't converge and/or the fit does not describe the raw data well.

Check the separate file How to adapt PeakPerformance to your data.

How to contribute

If you encounter bugs while using PeakPerformance, please bring them to our attention by opening an issue. When doing so, describe the problem in detail and add screenshots/code snippets and whatever other helpful material you can provide. When contributing code, create a local clone of PeakPerformance, create a new branch, and open a pull request (PR).

How to cite

Head over to Zenodo to generate a BibTeX citation for the latest release. A publication has just been submitted to a scientific journal. Once published, this section will be updated.

Programming language
  • Python 100%
  • AGPL-3.0-or-later
</>Source code

Participating organisations

Forschungszentrum Jülich

Reference papers


Jochen Nießer
Forschungszentrum Jülich GmbH
Michael Osthege
Forschungszentrum Jülich GmbH
Stephan Noack
Project Supervisor
Forschungszentrum Jülich GmbH