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Abstract:
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Single cell RNA sequencing (scRNA-seq) data provides an unprecedented resolution of measuring the gene expression heterogeneity at a single cell level. However, such data, unlike transcriptomic data from bulk samples contain genes either very high counts or very low counts within individual cells. So using the computational approaches to analyze bulk RNA-seq data is not appropriate for the analysis of scRNA-seq data. In this talk we will provide a brief description of the existing methods thus far for the analysis of scRNA-seq data. Moreover, we will a provide a proof-of-concept about developing novel statistical models for high dimensional and zero inflated scRNA-seq data to identify differentially expressed genes across cell types, which may contribute to cell-specific phenotypes and cell-cell interactions in complex biological processes such as memory circuit during learning. We will develop a decision theoretic optimal unsupervised clustering algorithm to characterize cell subpopulation using scRNA-seq data.
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