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.