Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary tool that allows us to address scientific questions that were elusive just a few years ago. Specifically, the scRNA-seq technology has the potential to enable insights into how a single cell develops into a complex organism, how cell-to-cell variation functions to maintain homeostasis, and how diseases such as cancer develop resistance to treatment. Unfortunately, much of this potential has yet to be realized as statistical methods to analyze scRNA-seq data are lacking. For many types of analyses, the methods currently in use obscure and, in some cases, distort biological signals. A number of statistical and computational challenges must be addressed to prevent inaccurate conclusions, and to optimize novel discovery. This proposal addresses those challenges. In particular, we propose normalization methods to adjust scRNA-seq data for artifacts and thereby ensure robust and accurate downstream inference. We also propose statistical methods to characterize and remove variability induced by oscillatory genes which can mask effects under study. By de-noising the data, these methods will improve the power with which signals of interest can be studied. Finally, we propose methods for identifying cell sub-populations and characterizing their differences across biological conditions. These types of methods are required for identifying novel sub-populations and for characterizing the ways in which cell- specific expression changes in response to various environments. Successful completion of the project will help to ensure that maximal information is obtained from powerful scRNA-seq experiments.

Public Health Relevance

Single-cell RNA-sequencing (scRNA-seq) is a revolutionary new technology that allows us to address scientific questions associated with embryogenesis, differentiation, and disease that were elusive just a few years ago. However, to achieve its potential, novel statistical methods are required for scRNA-seq data analysis. The proposed methods address some of the most important statistical and computational deficits facing investigators using scRNA-seq. Their development is required to prevent inaccurate inference and to ensure that maximal information is obtained from scRNA-seq experiments.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM102756-06
Application #
9321929
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Brazhnik, Paul
Project Start
2012-08-01
Project End
2020-07-31
Budget Start
2017-08-01
Budget End
2018-07-31
Support Year
6
Fiscal Year
2017
Total Cost
Indirect Cost
Name
University of Wisconsin Madison
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715
Bacher, Rhonda; Leng, Ning; Chu, Li-Fang et al. (2018) Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments. BMC Bioinformatics 19:380
Keller, Mark P; Gatti, Daniel M; Schueler, Kathryn L et al. (2018) Genetic Drivers of Pancreatic Islet Function. Genetics 209:335-356
Bacher, Rhonda; Chu, Li-Fang; Leng, Ning et al. (2017) SCnorm: robust normalization of single-cell RNA-seq data. Nat Methods 14:584-586
Gasch, Audrey P; Yu, Feiqiao Brian; Hose, James et al. (2017) Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress. PLoS Biol 15:e2004050
Choi, J; Ye, S; Eng, K H et al. (2017) IPI59: An Actionable Biomarker to Improve Treatment Response in Serous Ovarian Carcinoma Patients. Stat Biosci 9:1-12
Ye, Shuyun; Bacher, Rhonda; Keller, Mark P et al. (2017) Statistical Methods for Latent Class Quantitative Trait Loci Mapping. Genetics 206:1309-1317
Leng, Ning; Choi, Jeea; Chu, Li-Fang et al. (2016) OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data. Bioinformatics 32:1408-10
Tian, Jianan; Keller, Mark P; Broman, Aimee Teo et al. (2016) The Dissection of Expression Quantitative Trait Locus Hotspots. Genetics 202:1563-74
Bacher, Rhonda; Kendziorski, Christina (2016) Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol 17:63
Chu, Li-Fang; Leng, Ning; Zhang, Jue et al. (2016) Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm. Genome Biol 17:173

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