Dongjun Chung PhD, Assistant Professor in the Department of Biomedical Informatics at Ohio State University will be giving a presentation on “Statistical Methods for the Single-Cell TCR-seq Data Analysis and the Spatial Transcriptomics Experimental Design.” This presentation is entirely virtual, and will occur on June 18, 2024, 10:00am - 11:00am via Zoom. Registration is required, and upon registration, attendees will received a confirmation email ith information about joining the meeting.

ABSTRACT: Dr. Dongjun Chung will discuss two recent research works in his lab on spatial and single-cell multi-omics. First, single-cell RNA sequencing (scRNA-seq) data has been widely used for cell trajectory inference. However, the inferred trajectory may not reveal differentiation heterogeneity among T cell clones. Single-cell T cell receptor sequencing (scTCR-seq) data provides invaluable insights into the clonal relationship among cells, yet it lacks functional characteristics. Therefore, scRNA-seq and scTCR-seq data complement each other in improving trajectory inference. In this talk, Dr. Chung will discuss the novel statistical framework for the integrative analysis of scTCR-seq and scRNA-seq data, which allows researchers to explore clonal differentiation trajectory heterogeneity. Second, the recent advancement of high-throughput spatial transcriptomics (HST) technologies has allowed simultaneous measurement of close-to-cell-level gene expressions and spatial locations of these cells within a tissue or organ, providing an unprecedented opportunity to investigate spatial heterogeneity of cells within a tissue/organ. While various statistical methods for HST data analysis have been actively developed, a rigorous statistical framework for the design of HST experiments is still missing in the literature. However, researchers planning an HST experiment need to determine various experimental design parameters, such as the sequencing depth, and these choices need to be carefully made to achieve key goals of HST experiments, e.g., tissue architecture identification. In this talk, Dr. Chung discusses his newly developed statistical framework for the design of HST experiments, which aims to address this critical need.