6月17日10:00

With the advances in single cell technologies, cells are profiled through multiple modalities, and data on samples from an increasing number of individuals are obtained. I will present our method, scDisInFact, that disentangles variation in multi-batch multi-condition scRNA-seq datasets, and predicts data under unseen conditions. I will also present a few methods developed in our group on spatial transcriptomics data, including CLARIFY, that learns cell-cell interactions, scHybridNMF, that learns joint embedding of cells based on cell transcriptomics and locations. Finally, I will present scMultiSim, that can be used to evaluate methods for multi-omics and spatial data.

Xiuwei Zhang is a J.Z. Liang Early Career Chair Assistant Professor at the School of Computational Science & Engineering at Georgia Tech. She obtained her PhD in computer science from EPFL (École Polytechnique Fédérale de Lausanne) in Switzerland, and conducted postdoctoral research at Cambridge, UK and UC Berkeley. Her research focuses on developing methods to analyze single cell genomics and spatial data, including methods to study cell temporal dynamics, to perform data integration and to infer molecular interactions.