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hplc-py

hplc-py: A Python Utility For Rapid Quantification of Complex Chemical Chromatograms - Published in JOSS (2024)

https://github.com/cremerlab/hplc-py

Science Score: 98.0%

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    Found 7 DOI reference(s) in README and JOSS metadata
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    Links to: joss.theoj.org
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    Published in Journal of Open Source Software

Keywords

analytical-chemistry chromatography-analysis hplc

Scientific Fields

Mathematics Computer Science - 37% confidence
Last synced: 6 months ago · JSON representation ·

Repository

A Python utility for the processing and quantification of chromatography data

Basic Info
Statistics
  • Stars: 55
  • Watchers: 3
  • Forks: 9
  • Open Issues: 3
  • Releases: 11
Topics
analytical-chemistry chromatography-analysis hplc
Created over 2 years ago · Last pushed 8 months ago
Metadata Files
Readme License Citation

README.md

License: GPL v3 Run tests codecov PyPI version PyPI Downloads DOI

About

High-Performance Liquid Chromatography (HPLC) is an analytical technique which allows for quantitative characterization of the chemical components of a mixture. While many of the technical details of HPLC are now automated, the programmatic cleaning and processing of the resulting data often requires extensive manual labor. This package was developed to alleviate some of this burden, making the actual running of the HPLC the most time-consuming part of the quantification.

Installation

You can install hplc-py on pip:

$ pip install --upgrade hplc-py

Usage

Hplc-py has extensive documentation available with a quickstart, example calibration curve, step-by-step description of the algorithm, and API documentation.

The meat of hplc-py is in its Chromatogram class which performs all peak detection and fitting steps. There is an additional load_chromatogram function which makes it easy to read in chromatograms from a long-form tidy .csv file.

Loading and Viewing Chromatograms

For example, say you have a chromatogram file example.csv with columns of time and signal that looks like this:

time,signal 0, 0 0.00833, 0 0.01667, -1 ... 12.77500,668 12.78333,746 12.79167,830 12.80000,919 12.80833,1015 12.81667,1118 12.82500,1228 12.83333,1345 12.84167,1471 ...

Using hplc-py, this can be read from the file and loaded as a Chromatogram object as

python In[1]: from hplc.io import load_chromatogram In[2]: from hplc.quant import Chromatogram In[3]: example = load_chromatogram('example.csv', cols=['time', 'signal']) In[4]: chrom = Chromatogram(example)

The chromatogram is now loaded as an hplc-py Chromatogram object, which has a variety of methods annotated in the documentation. The chromatogram can be viewed by calling the show() method

python In[5]: chrom.show() Out[5]: [<Figure size 1280x960 with 1 Axes>, <Axes: xlabel='time', ylabel='signal'>] yielding:

Quantifying Peaks

Peaks can be automatically detected and fit in a chromatogram by calling the fit_peaks method of a Chromatogram object. Doing so on our example chromatogram yields a pandas DataFrame with the peaks and their properties

```python In[6]: peaks = chrom.fit_peaks() Performing baseline correction: 100%|████████| 299/299 [00:01<00:00, 167.06it/s] Deconvolving mixture: 100%|███████████████████████| 3/3 [00:13<00:00, 4.66s/it]

In[7]: peaks.head() Out[7]:
retentiontime scale skew amplitude area signalmaximum peak_id 10.90 0.158768 0.691961 23380.386403 2.805646e+06 66064.361454 1 13.17 0.592828 3.889788 43048.461053 5.165815e+06 50331.167860 2 14.45 0.350139 -2.997977 34791.996875 4.175040e+06 65352.588796 3 15.53 0.308900 1.567356 14938.309504 1.792597e+06 26723.707626 4 16.55 0.272283 1.261303 9512.727882 1.141527e+06 18121.299375 5 ```

The resulting chromatogram reconstruction can be seen by again calling the show() method, this time restricting the plotted time region between 10 and 20 minutes.

python In[8]: chrom.show([10, 20]) Out[8]:[<Figure size 640x480 with 1 Axes>, <Axes: xlabel='time', ylabel='signal (baseline corrected)'>]

Citation

If you use hplc-py in your research please cite the version of the software you use along with our descriptive paper in JOSS:

@article{chure2024, author = {Chure, Griffin and Cremer, Jonas}, title = {hplc-py: A Python Utility For Rapid Quantification of Complex Chemical Chromatograms}, volume = 9, number = 94, url = {https://joss.theoj.org/papers/10.21105/joss.06270}, doi = {10.21105/joss.06270}, number = {94}, urldate = {2024-02-16}, journal = {Journal of Open Source Software}, year = {2024} }

Contributing

Development of hplc-py occurs on various feature branches which are merged and released upon approval by Griffin Chure (@gchure), the primary maintainer of the software.

Please submit issues and bug reports using the issue tracker. When filing an issue, provide a reproducible example that demonstrates the bug or problem. Feature requests can also be made through the issue tracker, though it is up to the discretion of the maintainers what is worth implementing.

For help with troubleshooting fitting of your chromatograms, please open an issue giving an explanation of what's giving you trouble and provide a minimal working example of the problem. You can also tag your issue with the troubleshooting flag.

License

This software is released under the GNU General Public License version 3 (GPLv3). The complete license is provided as LICENSE.txt, but a brief description is as follows:

``` hplc-py Copyright (C) 2024, Griffin Chure & Jonas Cremer

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/. ```

Owner

  • Name: cremerlab
  • Login: cremerlab
  • Kind: organization

JOSS Publication

hplc-py: A Python Utility For Rapid Quantification of Complex Chemical Chromatograms
Published
February 16, 2024
DOI
Volume 9, Issue 94, Page 6270
Authors
Griffin Chure ORCID
Department of Biology, Stanford University, CA, United States of America, For correspondence, contact griffinchure@gmail.com
Jonas Cremer ORCID
Department of Biology, Stanford University, CA, United States of America
Editor
Jeff Gostick ORCID
Tags
Analytical Chemistry Quantitative Methods HPLC Chromatography
View PDF Review Thread Software Archive

Citation (CITATION.cff) 

cff-version: 1.1.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Chure"
  given-names: "Griffin"
  orcid: "https://orcid.org/0000-0002-2216-2057"
- family-names: "Cremer"
  given-names: "Jonas"
  orcid: "https://orcid.org/0000-0003-2328-5152"
title: "hplc-py v0.2.5"
version: 0.2.5
doi: 10.5281/zenodo.8197910
date-released: 2023-07-30
url: "https://github.com/cremerlab/hplc-py"

GitHub Events

Total
  • Issues event: 2
  • Watch event: 30
  • Delete event: 2
  • Issue comment event: 8
  • Fork event: 3
Last Year
  • Issues event: 2
  • Watch event: 30
  • Delete event: 2
  • Issue comment event: 8
  • Fork event: 3

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 200
  • Total Committers: 2
  • Avg Commits per committer: 100.0
  • Development Distribution Score (DDS): 0.005
Past Year
  • Commits: 6
  • Committers: 1
  • Avg Commits per committer: 6.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
gchure g****e@g****m 199
Florian Huber 3****r 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 11
  • Total pull requests: 12
  • Average time to close issues: about 1 month
  • Average time to close pull requests: 44 minutes
  • Total issue authors: 11
  • Total pull request authors: 2
  • Average comments per issue: 5.09
  • Average comments per pull request: 0.5
  • Merged pull requests: 11
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 2
  • Average time to close issues: N/A
  • Average time to close pull requests: less than a minute
  • Issue authors: 2
  • Pull request authors: 1
  • Average comments per issue: 4.5
  • Average comments per pull request: 0.5
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • sebastian-hogeweg (1)
  • gchure (1)
  • derb12 (1)
  • ccourpalais (1)
  • liaochen1988 (1)
  • lionel42 (1)
  • connor-pl-delaney (1)
  • liquidcarbon (1)
  • flo-kr (1)
  • sardnar (1)
  • jonathanstathakis (1)
Pull Request Authors
  • gchure (11)
  • florian-huber (2)
Top Labels
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troubleshooting (2) enhancement (1) help wanted (1) bug (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 197 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 10
  • Total maintainers: 1
pypi.org: hplc-py

Python utilities for the processing and quantification of chromatograms from High Performance Liquid Chromatography (HPLC).

  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 197 Last month
Rankings
Dependent packages count: 7.6%
Forks count: 30.4%
Average: 36.7%
Stargazers count: 39.3%
Dependent repos count: 69.5%
Maintainers (1)
gchure
Last synced: 6 months ago

Dependencies

.github/workflows/pytest.yaml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
requirements.txt pypi
  • matplotlib ==3.7.0
  • numpy ==1.23.5
  • pandas ==1.5.3
  • scipy ==1.10.0
  • seaborn ==0.12.2
  • setuptools ==65.6.3
  • tqdm ==4.64.1
setup.py pypi