Biostatistics Seminar Series

Translation of Single-Cell Genomics into Human Health - Mingyao Li, University of Pennsylvania

Thursday 9/12 3:30PM - 4:30PM
Public Health Lecture Hall (A115)

Translation of single-cell genomics into human health: methods and applications

Abstract: Recent technological breakthroughs have made it possible to measure gene expression at the single-cell level, thus allowing biologists and clinicians to better understand cellular heterogeneity and modify cell behavior through targeted molecular therapies. However, single-cell RNA sequencing protocols are complex. Even with the most sensitive platforms, the data are often noisy owing to a high frequency of dropout events, and the phenomenon of transcriptional bursting in which pulses of transcriptional activity are followed by inactive refractory periods. In this talk, I will present several statistical and machine learning methods that aim to tackle these challenges for a better understanding of cellular heterogeneity. I will illustrate our methods by showing results from ongoing collaborations on age-related macular degeneration and Alzheimer’s disease. With the growing interest in utilizing single-cell technologies in biomedical research, our methods will aid biomedical researchers to answer medically related questions and make exciting discoveries.

Last Updated On Tuesday, September 3, 2019 by Tang, Lu
Created On Monday, August 12, 2019