Resolving single-cell analysis challenges via data-driven decision frameworks and novel statistical methods

Resolving single-cell analysis challenges via data-driven decision frameworks and novel statistical
methods
Abstract:
Despite the power of single-cell RNA-seq, the data presents a multitude of analytical challenges and
researchers continue to struggle with data analysis. The long-term goals of this research program are to
develop robust, efficient, and scalable statistical methods and tools that enable all scientists to obtain accurate
biological inferences. Specifically, we propose to develop interactive data-driven decision-frameworks to guide
researchers through analyses and make informed analytical decisions. We also propose developing methods
that retain interpretability while accommodating complex experimental designs. All of our approaches will be
developed as highly accessible statistical software with interactive visualization and analysis modules available
via webservers. Our proposed methods will result in richer analyses and biological insights, as well as,
improved reproducibility and reliability of scientific results.

Grant Number: 5R35GM146895-04
NIH Institute/Center: NIH
Principal Investigator: Rhonda Bacher