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Abstract:
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Gene expression studies have yielded a number of fundamental insights that have changed the course of cancer diagnosis, prognosis, and treatment. In spite of their utility, traditional expression experiments are limited to measurements averaged over thousands of cells, which can mask or even misrepresent signals of interest. Fortunately, recent technological advances now allow us to obtain transcriptome-wide data from individual cells. While the data obtained from single-cell RNA-sequencing (scRNA-seq) is often structurally identical to that from a bulk expression experiment, the relative paucity of starting material and increased resolution give rise to distinct features in scRNA-seq data. These features, in turn, pose both opportunities and challenges for which novel statistical and computational methods are required. In this talk, I will highlight the methods from bulk RNA-seq that are commonly used for scRNA-seq normalization, the disadvantages in doing so, and a method we recently developed to enable robust normalization for scRNA-seq experiments.
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