Olga Vitek (Presenter)
Northeastern University
Bio: Dr. Vitek holds a PhD in Statistics from Purdue University. She is currently a Sy and Laurie Sternberg Interdisciplinary Associate Professor at Northeastern University, with a joint appointment in the College of Science and the College of Computer and Information Science. Dr. Vitek’s group develops statistical methods and algorithms for quantitative mass spectrometric investigations. The methods optimize the experimental design, and ensure accurate and objective interpretation of the resulting large and complex datasets. The methods are available through free and open-source software, including Cardinal, an R package for statistical interpretation of mass spectrometry-based imaging experiments. Dr. Vitek serves on the board of directors of the US Human Proteome Organization.
Authorship: O. Vitek
Northeastern University, Boston, MA
| Short Abstract Statistical methods are key for detecting signals (e.g., caused by an intervention or a disease) in presence of variation and uncertainty. This is particularly important for mass spectrometry-based imaging, where signals are obscured by variation between different biological replicates, the spatial variation within images of a same biological replicate, and the technical variation due to sample handling and spectral acquisition. Moreover, as spatial and mass resolution increase, the experiments become more prone to generating spurious associations, and to amplifying bias and confounding. This talk will discuss the importance of statistical inference when designing and analyzing mass spectrometry-based imaging experiments, as well as statistical methods and open-source software designed to facilitate the statistical inference tasks. |
Long Abstract
Statistical methods for mass spectrometry-based imaging
Statistical methods are key for detecting systematic signal (e.g., caused by an intervention or a disease) in presence of variation and uncertainty, and for making objective and reproducible conclusions. This is particularly important for mass spectrometry-based imaging, where signals are obscured by 3 types of variation: the variation between different biological replicates, the spatial variation within images of a same biological replicate, and the technical variation due to sample handling and spectral acquisition. Moreover, the large-scale nature of mass spectrometry imaging experiments presents an additional challenge. As spatial and mass resolution increase, the experiments become more prone to generating spurious associations, and to amplifying bias and confounding. This talk will discuss the importance of statistical inference when designing and analyzing mass spectrometry-based imaging experiments, as well as statistical methods and open-source software designed to facilitate the statistical inference tasks.
References & Acknowledgements:
K. D. Bemis, A. Harry, L. S. Eberlin, C. Ferreira, S. M. van de Ven, P. Mallick, M. Stolowitz, O. Vitek. “Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments”. Bioinformatics, 31:2418, 2015.
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| Grants | no | |
| Salary | no | |
| Board Member | no | |
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IP Royalty: no
| Planning to mention or discuss specific products or technology of the company(ies) listed above: | no |