A UNIFIED STATISTICAL FRAMEWORK FOR SINGLE CELL AND BULK RNA SEQUENCING DATA

The Annals of Applied Statistics
Lingxue ZhuKathryn Roeder

Abstract

Recent advances in technology have enabled the measurement of RNA levels for individual cells. Compared to traditional tissue-level bulk RNA-seq data, single cell sequencing yields valuable insights about gene expression profiles for different cell types, which is potentially critical for understanding many complex human diseases. However, developing quantitative tools for such data remains challenging because of high levels of technical noise, especially the "dropout" events. A "dropout" happens when the RNA for a gene fails to be amplified prior to sequencing, producing a "false" zero in the observed data. In this paper, we propose a Unified RNA-Sequencing Model (URSM) for both single cell and bulk RNA-seq data, formulated as a hierarchical model. URSM borrows the strength from both data sources and carefully models the dropouts in single cell data, leading to a more accurate estimation of cell type specific gene expression profile. In addition, URSM naturally provides inference on the dropout entries in single cell data that need to be imputed for downstream analyses, as well as the mixing proportions of different cell types in bulk samples. We adopt an empirical Bayes' approach, where parameters are estimated using the EM a...Continue Reading

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Feb 20, 2020·Bioinformatics·Junlin XuJiaLiang Yang
Nov 7, 2019·Briefings in Bioinformatics·Bo Sun, Liang Chen
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Jun 24, 2021·Briefings in Bioinformatics·Yaxuan CuiYong Chen

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