  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.
