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Pathomx: an interactive workflow-based tool for the analysis of metabolomic data

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 extensible environment.

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 hypoxic conditions.

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.


Getting Started with Pathomx 08.12.2014

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 ...


PyQtConfig: A simple API for keeping your PyQt Widgets and config in sync 21.11.2014

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 v3.0.2 released 26.10.2014

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.


Pathomx v3.0.0 Release Candidate 2 07.10.2014

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!


Pathomx v3.0.0alpha4 for Mac 15.07.2014

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.


PyQt5 support in Matplotlib 27.06.2014

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.




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