Rhonda Bacher, Ph.D., has received the Maximizing Investigators’ Research Award from the National Institute of General Medicine Sciences. The goal of the awards program is to provide investigators with greater stability and flexibility, thereby enhancing scientific productivity and the chances for important breakthroughs.
Bacher, an assistant professor in the department of biostatistics in the UF College of Public Health and Health Professions and the UF College of Medicine, is believed to be the first PHHP faculty member to receive the award, which is presented to highly talented and promising investigators. The five-year, $1.86 million award will support Bacher’s development of data-driven decision frameworks and novel statistical methods to resolve challenges faced by scientists when analyzing single cell data.
“With this project, we plan to develop new statistical methods that are flexible in handling more complex experimental designs and bring more context and interpretation to biological questions in single-cell data,” Bacher said.
Single-cell RNA sequencing is an experimental technique that allows researchers to understand how genes are expressed at the individual cell level. It has been widely used in immunological studies to better understand how different cell types respond to treatments and various illnesses, such as Type 1 diabetes, sepsis and COVID-19 and other viruses, as well as in cancer research, Bacher said.
But researchers can run into problems when it comes time to select from among the hundreds, or possibly thousands, of tools to analyze their single-cell RNA sequencing data.
“Each of these methods has its own set of hyperparameters, pre-processing requirements and assumptions,” Bacher said. “Because of this, all-inclusive or automated pipelines have become increasingly attractive to investigators. However, these can lead to ill-suited analyses or even incorrect biological conclusions. One of our goals with this project is to develop accessible, interactive analytical platforms to guide researchers through various decisions and evaluations that arise in the course of single cell RNA sequencing analyses. This will enable richer analyses of high-throughput genomics data while improving reproducibility and reliability of scientific results.”