= Emerging. More than 5 years before clinical availability. (26.55%)
= Expected to be clinically available in 1 to 4 years. (39.66%)
= Clinically available now. (33.79%)
MSACL 2020 US : Vitek

MSACL 2020 US Abstract

Keynote Presentation

Topic: Data Science

Podium Presentation in Room 3 on Wednesday at 9:00 (Chair: Randall Julian)

Components of Reproducible Quantitative Mass Spectrometry-based Proteomics: A Statistician’s Perspective

Olga Vitek (Presenter)
Northeastern University

Presenter Bio(s): Dr. Olga Vitek holds a PhD in Statistics from Purdue University. She is Professor in the Khoury College of Computer Sciences at Northeastern University, and was previously a Faculty and a University Faculty Scholar at Purdue. Her research intersects statistical science, machine learning, mass spectrometry and systems biology. Statistical methods and open-source software developed in her lab are widely used in academia and industry. Dr. Vitek is a Senior Member of the International Society for Computational Biology, is an Elected Member of the Council of HUPO and of the Board of Directors of USHUPO. She is a member of the Editorial advisory board of Molecular and Cellular Proteomics and of Journal of Proteome Research.

Authors: Olga Vitek
Northeastern University

Abstract

Quantitative mass spectrometry-based proteomics aims to distinguish systematic variation in protein abundance (due, e.g., to a treatment or a disease) from nuisance biological and technological variation. Statistical mindset is key for doing so in both repeatable and reproducible manner. Frequently, statistical tasks are viewed as limited to detecting differentially abundant proteins. In reality, statistical components of reproducibility are substantially broader. They include all aspects of data processing (Which features should we use to quantify a protein? How should we combine the features into a protein-level conclusion?). They also include aspects of experimental design, from both biological perspective (Which proteins and samples, and how many, do we need to quantify?) and technological perspective (Are the assays appropriate for the task? Do the experimental steps run properly?). Answering these questions requires the availability of statistical methods, and but also of publicly available data that help understand the advantages and the limitations of the methodological choices. This talk will highlight the contributions of our lab to these components of reproducible research.


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