Affiliation : President and Founder, Denovo Software, USA
Title of the Talk/Lab :Overview of High Dimensional Data Analysis in Flow Cytometry
David Novo received his PhD in Biophysics from UCLA. His research concentrated on developing novel optical and mathematical techniques for measurements and analysis of localized fluorescent Calcium transients from a microdomain within a muscle cell. Prior to commencing his PhD, David worked with Dr. Howard Shapiro, one of the leaders in the field of flow cytometry, performing research on membrane potential determination in bacteria using flow cytometry. During the course of this research they built several flow cytometers custom made for bacterial measurements. Two papers were published on the subject. For the last 20 years, David has been the president of De Novo Software, which produces data analysis software for Flow and Image Cytometry. David still maintains close ties to the flow cytometry community through his appointment to the Data Standards Committee of the International Society of Analytical Cytometry. David frequently consults on data analysis issues related to flow cytometry and collaborates on many projects involving novel data analysis requirements.
Lecture and Lab: Overview of High Dimensional Data Analysis in Flow Cytometry: This presentation will briefly review the history of data analysis techniques, denoting key increases in the level of mathematical sophistication from simple graphing of raw data, to applying mathematical models, to the current state of the art. The lecture will focus on the common classes of high dimensional data algorithms and an overview of the information they try to convey. It will also introduce the complexities involved in correctly applying and configuring these algorithms and attempt to give enough background to researchers that they will feel more comfortable applying this emerging technology to their data.