SCALABLE STATISTICAL METHODS AND SOFTWARE FOR SINGLE-CELL AND SPATIAL DATA SCIENCE
STEPHANIE HICKS – JOHNS HOPKINS UNIVERSITY
Single-cell RNA-Seq (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. However, single-cell data present unique challenges that have required the development of specialized methods and software infrastructure to successfully derive biological insights. Compared to bulk RNA-seq, there is an increased scale of the number of observations (or cells) that are measured and there is increased sparsity of the data, or fraction of observed zeros. Furthermore, as single-cell technologies mature, the increasing complexity and volume of data require fundamental changes in data access, management, and infrastructure alongside specialized methods to facilitate scalable analyses. I will discuss some challenges in the analysis of scRNA-seq and spatially-resolved transcriptomics data and present some solutions that we have made towards addressing these challenges.
Dr. Stephanie Hicks is an Associate Professor in the Department of Biostatistics at Johns Hopkins BSPH where she is also a faculty member of the Johns Hopkins Data Science Lab, and has affiliations with the Malone Center for Engineering in Healthcare, Center for Computational Biology, the Department of Genetic Medicine, and the Department of Biochemistry and Molecular Biology. She is a expert in developing scalable computational methods and open-source software for biomedical data analysis, in particular single-cell and spatial transcriptomics genomics data, leading to an improved understanding of human health and disease. She serves on a variety boards including the Bioconductor Technical Advisory Board, and the Editorial Board at Genome Biology and the Journal of American Statistical Association. Locally, she co-founded and co-organizes the R-Ladies Baltimore chapter to promote gender diversity in the R programming language community. She is a recipient of several professional awards including a K99/R00 Pathway to Independence Award, a High-Impact Project Award from the Bloomberg American Health Initiative, Teaching in the Health Sciences Young Investigator Award from the American Statistical Association (ASA), and the the COPSS Emerging Leader Award from the ASA, arguably the statistical profession’s most prestigious award for early career leaders in Statistics and Data Science.