Congratulations to Yingying Lu! She passed her PhD Final Defense, 07/27/2023, 1:00 PM
Title: Cell Type Deconvolution Using Single-Cell RNA-Seq Data
Abstract: High-throughput sequencing technologies like RNA-Seq offer an extensive analysis of gene expression, yet they face limitations when analyzing samples at the single cell level. Cell type deconvolution helps to tackle this by estimating the proportions of different cells present in tissue samples. This dissertation introduces two novel deconvolution methods: one for bulk RNA-Seq data, named SECRET, and another for spatial transcriptomics, called SPADE. SECRET, a semi-referenced approach, is applied to the analysis of metastatic cancers. It enhances the accuracy of proportion estimation by considering the unknown cell type. The second method, named SPADE, is tailored for spatial transcriptomics. It integrates spatial location and histology, leading to a more refined understanding of cell composition. These methods hold potential to enrich our understanding of the biological mechanisms that govern gene expression and their regulation.
Zoom link : https://arizona.zoom.us/j/2700216059