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 also present our recently developed methods on integrative analysis of single-cell multi-omics data. I will illustrate our methods by showing results from ongoing collaborations on Alzheimer’s disease and cardiometabolic 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.

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