NIH R01 · 2024
Developing tools for the unbiased analysis and visualization of scRNA-seq data
ABSTRACT Single-cell RNA sequencing (scRNA-seq) provides genome-wide information about gene expression at the resolution of individual cells. The unprecedented scope of these data is revolutionizing our understanding of development and tissue homeostasis as well as diseases like cancer. A major issue with scRNA-seq, however, is the shear scale of the data, consisting of ~20,000 gene expression measurements in thousands to millions of cells. Effective computational approaches are clearly required to translate data of this size and complexity into actionable biological insights. For instance, scRNA-seq data are approximately 20,000-dimensional, and as a result all available analysis pipelines…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.