|Eralp Dogu Ph.D. is an assistant professor of statistics in the College of Science at Mugla Sitki Kocman University (Turkey). He holds a B.S. degree in Statistics, a M.S. degree in Statistics, and a Ph.D. in Statistics from Dokuz Eylul University (Turkey). He joined Marcus Department of Industrial and Manufacturing Engineering at Penn State as a visiting scientist in 2009. He also conducted research at the Center for Integrated Healthcare Delivery Systems (CHIDS) as a CHIDS scholar during 2009-2010. Read More |
In 2014, he joined the Healthcare Systems Engineering (HSyE) Institute at Northeastern University as a postdoctoral fellow. In 2015, Eralp joined the Olga Vitek lab for Statistical Methods for Studies of Biomolecular Systems at Northeastern University, where he developed MSstatsQC, an R based software to analyze system suitability for proteomic experiments, as a postdoctoral fellow.
MSstatsQC: An R-based Tool to Monitor System Suitability and Quality Control Results for Targeted Proteomic Experiments
MSstatsQC is an open-source R-based software package and a web-based graphical user interface for monitoring data quality of targeted experiments. It helps user to monitor (i) longitudinal system suitability testing (SST) results which verify that mass spectrometric instrumentation performs as specified and (ii) quality control (QC) results which provides in-process quality assurance of the sample profile. MSstatsQC is available as a stand-alone tool from www.msstats.org/msstatsqc. It is compatible with Skyline custom reports and additionally, its functionalities are available to the users as part of Panorama AutoQC. MSstatsQC translates modern methods of longitudinal statistical monitoring, such as simultaneous and time weighted control charts and change point analysis, to the context of LC-MS experiments, discusses their advantages, and provides practical guidelines for overall decision making. In this presentation the functionalities of MSstatsQC, how it can be used to monitor data quality in targeted proteomic experiments, and its implementation into Panorama AutoQC are discussed.