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March 2018 A unified statistical framework for single cell and bulk RNA sequencing data
Lingxue Zhu, Jing Lei, Bernie Devlin, Kathryn Roeder
Ann. Appl. Stat. 12(1): 609-632 (March 2018). DOI: 10.1214/17-AOAS1110


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 algorithm and approximate inference is obtained by Gibbs sampling. Simulation results illustrate that URSM outperforms existing approaches both in correcting for dropouts in single cell data, as well as in deconvolving bulk samples. We also demonstrate an application to gene expression data on fetal brains, where our model successfully imputes the dropout genes and reveals cell type specific expression patterns.


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Lingxue Zhu. Jing Lei. Bernie Devlin. Kathryn Roeder. "A unified statistical framework for single cell and bulk RNA sequencing data." Ann. Appl. Stat. 12 (1) 609 - 632, March 2018.


Received: 1 September 2016; Revised: 1 March 2017; Published: March 2018
First available in Project Euclid: 9 March 2018

zbMATH: 06894720
MathSciNet: MR3773407
Digital Object Identifier: 10.1214/17-AOAS1110

Keywords: EM algorithm , Empirical Bayes , Gibbs sampling , hierarchical model , Single cell RNA sequencing

Rights: Copyright © 2018 Institute of Mathematical Statistics


Vol.12 • No. 1 • March 2018
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