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IBEC Seminar: Alexandre Perera
Thursday, December 3, 2015 @ 10:00 am–11:00 pm
Data evaluation in Metabolomics, preprocessing, analysis and biological enrichmentAlexandre Perera, B2SLab Bioinformatics and Biomedical Signals Laboratory, UPC
This talk will depict the last efforts by the B2Slab on the processing of LC/MS metabolomics data. First, we describe a new method to solve known issues of peak intensity drifts in metabolomics datasets. This method is based on a two-step approach in which intensity drift effects are modelled through Common Principal Components Analysis and removed from original data. Secondly, we propose a new processing workflow based on peak aggregation techniques. We show that the predictive power of the data is improved when the peak aggregation techniques are used regardless of the prediction technique used. We also describe a new computational tool to perform end-to-end analysis (MAIT) coded under the R environment. MAIT package is highly modular and programmable which allow the users to perform their personalised LC/MS data analysis workflows. MAIT is able to take the raw output files from an LC/MS instrument as an input and, by applying a set of functions, provide a metabolite identification table as a result. Finally, we introduce FELLA, a set of algorithms for biological interpretation of metabolomic data in light of existing knowledge extracted from annotation databases, extending the concept of pathway enrichment into metabolomics. FELLA is based on diffusion process on a graph representation of a knowledge base, while statistically testing solutions against analytical null diffusion distributions. Results are provided comparing the tools with sate of the art methods on different network types.
 Fernández-Albert F., Llorach R., Andrés-Lacueva C., Perera-Lluna A. Peak Aggregation as an Innovative Strategy for Improving the Predictive Power of LC-MS Metabolomic Profiles. Analytical Chemistry 86 (5), 2320–5 (2014).
 Fernández-Albert F., Llorach R., Andrés-Lacueva C., Perera-Lluna A. An R package to analyse LC/MS metabolomic data: MAIT (Metabolite Automatic Identification Toolkit). Bioinformatics 30(13):1937-9 (2014).
 Fernández-Albert F., Llorach R., Garcia-Aloy M, Ziyatdinov A, Andrés-Lacueva C., Perera-Lluna A. Intensity drift removal in LC/MS metabolomics by Common Variance Compensation. Bioinformatics 30(20), 2898-2905 (2014)
 Domingo-Almenara, X., Perera, A., Ramírez, N., Cañellas, N., Correig, X., & Brezmes, J. (2015). Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation. Journal of Chromatography A, 1409, 226-233
 Ziyatdinov, A.; Marco, S.; Chaudry, A.; Persaud, K.; Caminal, P.; Perera, A. Drift compensation of gas sensor array data by common principal component analysis. Sensors and Actuators B: Chemical 146, 460-5 (2010).