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

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

SUBMITTER: Zhu L 

PROVIDER: S-EPMC6114100 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature