Abstract:

    Single cell genomics technologies have revolutionized the biomedical research, and the development of analytical methods for single cell data has been the most active and cutting-edge area in the computational biology field. A majority of the existing methods were developed based on data with small sample size (one or a few subjects). With the technological advances and cost reduction, people start to perform large-scale, population level single cell experiments.

    These data have extra layers of complexities, for example, the demographics and phenotypes of the subjects, experimental design (crossed, nested, paired, longitudinal), etc. Up to now, there is no consensus on the best strategy for analyzing these types of data. In this talk, I will share our recent experiences in analyzing population-level single cell RNA-seq (scRNA-seq) data. I will share our observations, thoughts, and some works in method development in several aspects of the data analyses.