Friday February 4, 2022
1:00PM Eastern Time
PhD Candidate, Department of Biostatistics, Epidemiology and Informatics
University of Pennsylvania
ABSTRACT: The advent of high-throughput next-generation sequencing (NGS) technologies has transformed our understanding of cell biology and human disease. As NGS has been adopted earliest by the scientific community, its use has now become widespread, and the technology has improved rapidly. At present, it is now common for laboratories to assay genome-wide transcriptomes of thousands of cells in a single scRNA-seq experiment. In addition, technologies that enable the measurement of new information, for example, chromatin accessibility, protein quantification, and spatial location, have been developed. In order to take full advantage of the multi-modality information when analyzing NGS data, new methods are demanded. This seminar will introduce several machine learning algorithms for NGS data analysis with different aims, including cell type classification, spatial domain detection, and tumor microenvironment annotation.
Parts of the talk are based on the following two papers:
KEYWORDS: single cell RNA sequencing (scRNA-seq), Spatial transcriptomics (ST), tumor microenvironment, machine learning