Background Metabolomics is a systems approach to the analysis of
cellular processes through small-molecule metabolite profiling.
Standardisation of sample handling and acquisition approaches has
contributed to reproducibility. However, the development of robust
methods for the analysis of metabolomic data is a work-in-progress. The
tools that do exist are often not well integrated, requiring manual data
handling and custom scripting on a case-by-case basis. Furthermore,
existing tools often require experience with programming environments
such as MATLAB® or R to use, limiting accessibility. Here we present
Pathomx, a workflow-based tool for the processing, analysis and
visualisation of metabolomic and associated data in an intuitive and
Results The core application provides a workflow editor, IPython
kernel and a HumanCyc™-derived database of metabolites, proteins and
genes. Toolkits provide reusable tools that may be linked together to
create complex workflows. Pathomx is released with a base set of plugins
for the import, processing and visualisation of data. The IPython
backend provides integration with existing platforms including MATLAB®
and R, allowing data to be seamlessly transferred. Pathomx is supplied
with a series of demonstration workflows and datasets. To demonstrate
the use of the software we here present an analysis of 1D and 2D 1H NMR
metabolomic data from a model system of mammalian cell growth under
Conclusions Pathomx is a useful addition to the analysis toolbox.
The intuitive interface lowers the barrier to entry for non-experts,
while scriptable tools and integration with existing tools supports
complex analysis. We welcome contributions from the community.
This is quick start-up guide for new users of Pathomx. Following it should give you everything that you need to know to start using Pathomx right away. Once you’ve been through the basics you might like to see some of the demos to see what Pathomx is capable of ...
Introducing PyQtConfig: a simple API for handling, persisting and synchronising configuration within PyQt applications. This module was built initially as part of the Pathomx data analysis platform but spun out into a standalone module when it became clear it was quite useful. This post gives a brief overview of the ...
Pathomx is a workflow-based data analysis tool built on IPython. It began as a metabolomic-analysis toolkit, but has extended to support general data analysis workflows. It aims to be simple to use for non-experts while powerful enough for complex analysis tasks. Key to both of these goals is the ability ...
Pathomx v3.0.2 has been released for both Windows and MacOS X. This marks the first stable, bug-fixed release for the v3.0 line featuring the new IPython-kernel with cluster support for parallel processing of tools.
The final release candidate for Pathomx v3.0.0 is available for both Mac and Windows. This latest version features the new IPython backend providing parallel processing (via IPython ipcluster support), numerous bugfixes and improvements to the UI and figure outputs. While a development version it is considered stable enough for regular use. If you’re a current user of Pathomx, please download and test with your own hardware and data, see how it holds up and then report any problems!
A development version of Pathomx v3.0.0-alpha4 is now available as a installable app for Mac. This latest version showcases the new IPython backend, with notebooks and reports.
My pull-request for matplotlib to add PyQt5 support has been accepted and merged, meaning PyQt5 support will be available in the upcoming v1.4.0 release of matplotlib.
A new developer release of Pathomx (v3.0.0a) is out today via Github and PyPi. This release brings an IPython backend and support for IPython-notebook based plugins.
Another Python module released today. MetaboHunter is a Python module for accessing the MetaboHunter web service for automated assignment of 1D raw, bucketed or peak picked NMR spectra.
Page 1 / 9