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MIDAS Seminar Series Presents: Gerald Quon – University of California, Davis
November 19, 2019 @ 2:00 pm - 3:00 pm
Forum Hall, Palmer Commons
Assistant Professor, Department of Molecular and Cellular Biology
University of California, Davis
Quantifying cell type-specific changes in transcriptional state and gene co-regulation across multiple datasets using scRNA-seq
Abstract: I will first discuss my lab’s efforts to computationally characterize differences in cell type-specific gene regulation across conditions, tissues and species (or more generally, datasets). We have recently developed scAlign, a tool for performing single cell alignment and data integration to match cells of the same type across datasets. Compared to existing approaches, scAlign is unique in that it can leverage cell type labels for subsets of cells (derived from e.g. only high confidence markers), in addition to being capable of fully unsupervised (no cells are labeled) or fully supervised (all cells are labeled) alignment. We demonstrate diverse applications of scAlign, including finding conserved cell types between the human and mouse cortex, matching hematopoietic progenitor populations across control-stimulus conditions and identifying a rare population of P. falciparum cells that undergo late sexual commitment. I will also demonstrate tools we have developed for performing post-alignment analyses, such as finding differential gene modules and regulation across conditions.
In the second part of the talk, I will introduce a unifying deconvolution framework for addressing problems such as the spatial mapping of cell types from marker genes, purifying Patch-seq RNA measurements of contaminants and identifying cell type-specific gene expression changes from bulk RNA sequencing. Our framework constructs cell type-specific models of gene regulation from reference cell atlases, from which marker genes are extracted and used to identify the presence of specific cell types, as well as possible gene dysregulation, in new RNA measurements. Our preliminary experiments show our deconvolution approach leads to higher numbers of gene-electrophysiology correlations from Patch-seq data, and also show we can extract cell type-specific changes in gene regulation from bulk RNA samples of Alzheimer’s patients.