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The Software Sweet Spot for Metabolomics

Aug 21, 2019

I attended Metabolomics 2019 and was pleased to find a rapidly expanding discipline populated with very enthusiastic researchers. Applications ranged from developing plants with increased levels of nutrients to understanding cancer metabolism.

Metabolomics experiments, however, produce extremely large and complex data sets. Consequently, the ultimate success of any experiment in metabolomics hinges on the software used to analyze the data. It was not surprising to find that multivariate analysis methods were front and center in many of the presentations and posters.

At the conference I saw some nice examples using our software, but of course not as many as I would have liked. So when I got home I put together this table comparing our PLS_Toolbox and Solo software with SIMCA, R and Python for use with metabolomics data sets.

Compare Software for Metabolomics

Available for Windows,
Mac and Linux?
YesYesWindows onlyYes
Comes with User Support?YesYesYesNo
Point-and-click GUIs for
all important analyses?
Command Line available?YesNoNoYes, use is mandatory
Source Code available
for review?
YesYes, same
code as
Includes PCA, PLS, O-PLS?YesYesYesAdd-ons available
Includes ASCA, MLSCA?YesYesNoAdd-ons available
Includes SVMs, ANNs
and XGBoost?
YesYesNoAdd-ons available
Includes PARAFAC,
YesYesNoAdd-ons available
Includes Curve Resolution Methods? YesYesNoAdd-ons available
Extensible?YesNoYes, through PythonYes
Instrument standardization,
calibration transfer tools?
Comes complete?YesYesYesNo
Easy to install?YesYesYesNo

So it’s easy to see that PLS_Toolbox is in the sweet spot with regards to metabolomics software. Yes, it requires MATLAB, but MATLAB has over 3 millions users and is licensed by over 5000 universities world wide. And if you don’t care to use a command line Solo includes all the tools in PLS_Toolbox and doesn’t require MATLAB. Plus, Solo and PLS_Toolbox share the same model and data formats. So you can have people in your organization that use only GUIs work seamlessly with people who prefer access to the command line.

So the bottom line here is:

  • If you are just getting started with metabolomics data PLS_Toolbox and Solo are easy to install, include all the analysis tools you’ll need in easy to use GUIs, are transparent and are relatively inexpensive.
  • If you are using SIMCA, you should try out PLS_Toolbox because it includes many methods that SIMCA doesn’t have, the source code is available, its more easily extensible, works on all platforms, and it will save you money.
  • If you are using R or Python, you should consider PLS_Toolbox because it is fully supported by our staff, has all the important tools in one place, sophisticated GUIs, and is easy to install.

Ready to try PLS_Toolbox or Solo? Start by creating an account and you’ll have access to free fully functional demos. Questions? Write to me!