Presenter: Weiguang Mao, Ph.D. ( University of Pittsburgh)
Topic: Generative Models of Biological Variations in Bulk and Single-cell RNA-seq
The explosive growth of transcriptomic profiling has enabled an extensive characterization of transcriptome complexity at both bulk and single-cell levels. However, valuable biological signals are often convolved with technical and nuisance biological variations complicating inference in a high dimensional setting. In this talk I will present two of our recent works, PLIER and NIFA, which can extract and summarize the biological variations as reproducible and interpretable latent representations. These latent representations can greatly facilitate downstream analysis and provide additional biological insight. I will end by discussing how these compact representations can be helpful to design transcriptome signatures specific to SARS-CoV-2 infection based on host response.
