Causal Inference in Single-cell Genomics - Machine Learning in Computational Biology

Explore causal inference in single-cell genomics through this research seminar presented by Yongjin Park from the University of British Columbia. Delve into the unique aspects of single-cell RNA-seq data and discover how statisticians can leverage its special structure. Learn about a novel algorithm for ascertaining the effect of disease status on cell-type-specific gene expression profiles, and gain insights into various causal effect inference strategies. Examine scalable approaches for cell type assignment and the integration of single-cell data with existing tissue-level bulk data. Uncover how this integrative analysis provides high-resolution, cell-type-level views of complex disease mechanisms in genome-wide association studies. The seminar covers topics such as differential expression analysis, cellular context, biological covariates, single-cell mixture models, and self-annotation techniques.