New directions in single cell genomics method development

PROJECT SUMMARY
Single cell technologies, in particular single cell transcriptomics, have been applied to numerous areas in
biological and biomedical research and become a powerful tool for complex tissue characterization. Despite its
ever-growing throughput and complexity, the development of analytical tools for single cell genomics has fallen
behind the technological advances. The overarching goal of this proposal is to address some of the most
pressing analytic challenges facing profiling and interpreting single cell genomics data, including: 1) lack of
differential expression analysis methods that properly account for within-sample cellular heterogeneity; 2) lack
of cis-regulatory inference methods that leverage multi-omics data; and 3) lack of proper methods to perform
eQTL mapping in population-scale scRNA-seq studies. In the proposal, we will work on the following aims: Aim
1. Develop a differential expression analysis framework that better resolves sample heterogeneity and combats
false discoveries for single cell data. Aim 2. Develop Bayesian model selection methods that infer cis-
regulatory relationships from multi-omics data. Aim 3. Develop eQTL mapping methods that accommodate
multiple cell types and experimental conditions in population-scale scRNA-seq studies. All methods will be
implemented in user-friendly software and disseminated to the scientific community. Successful achievement
of Aims 1 and 2 will dramatically increase the power of routine single cell genomics analysis, facilitating the
application of these cutting-edge technologies to translational and clinical studies. Successful achievement of
Aim 3 will provide new ways to comprehensively characterize the genetic architecture underlying gene
expression that is specific to both cell-type and experimental-condition, ultimately facilitating the understanding
of common diseases and disease-related complex traits.

Grant Number: 5R01GM126553-08
NIH Institute/Center: NIH
Principal Investigator: Mengjie Chen