PAD

Peak Alignment via Density optimization

Authors: Venura Perera, Marta De Torres Zabala, Hannah Florance, Nick Smirnoff, Murray Grant, Zheng Rong Yang

Summary of the work

Motivation: Rapid improvements in mass spectrometry sensitivity and mass accuracy combined with improved liquid chromatography separation technologies allow acquisition of high throughput metabolomics data, adding a new opportunity to understand biological processes. While spectral de-convolution software can identify discrete masses and their associated isotopes and adducts, the utility of metabolomic approaches for many statistical analyses such as identifying differentially abundant ions depends heavily on data quality and robustness, especially, the accuracy of aligning features across multiple biological replicates.

Results: We have developed a novel algorithm for feature alignment using density maximization. In contrast to a greedy iterative, hence local, merging strategy, which has been widely used in the literature and in commercial applications, we apply a global merging strategy to provide better alignment quality. Using both simulated and real data, we demonstrate an important feature of our new algorithm, i.e. high map coverage. It is critically important for non-targeted comparative metabolite profiling of highly replicated biological datasets.

Acknowledgments: This work was partially funded by the Biotechnology and Biological Sciences Research Council grants BB/D007046 to M.G; BB/F005903 to M.G & N.S. and BB/F011652/1 to N.S. Some of this work was carried out under the Plant Health License 204B/6527.

The excutable code
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The README file for the program
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