Full-length, single-cell RNA data provides critical insights into the understanding of cancer transcriptomic features, such as isoforms, fusions, and expressed mutations. However, until recently, cell typing based on gene expression has been challenging with long reads due to insufficient sequencing depth.
In this on demand webinar Arthur Dondi discusses:
Register to watch on demand:
Ted Kalbfleisch, Ph.D., Associate Professor, University of Kentucky
Mitchell Feldmann, Ph.D., Postdoctoral Fellow, University of California, Davis.