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B0631
Title: Normalization and differential expression in single cell RNA-seq Authors:  Hao Wu - Emory University (United States) [presenting]
Zhijin Wu - Brown University (United States)
Abstract: Single cell RNA-seq (scRNA-seq) enables the transcriptomic profiling at individual cell level. This new level of resolution reveals inter-cellular transcriptomic heterogeneity and brings new promises to the understanding of transcriptional regulation mechanism. The special characteristics in scRNA-seq data, including excessive zeros, high variability, and multi-modal distribution, bring challenges in data analysis because typical assumptions made for bulk RNA samples are no longer hold. We will present a probabilistic model of sequencing counts that well explains the characteristics of single cell RNA-seq data. We will further present an adaptive normalization method that is robust to the bursting nature of expression in many genes, and a redefined differential expression procedure.