Abstract：Single-cell and spatial omics technologies now profile cells across multiple samples, conditions, tissue sections, time points, and modalities. These data offer new opportunities to study cellular variation, tissue organization, and dynamic biological processes, but they also require computational methods that can separate biological signals from technical confounding and integrate spatial and temporal information. In this seminar, I will present three projects from our group that address these challenges. scDisInFact disentangles condition effects from batch effects in multi-batch, multi-condition scRNA-seq data, enabling batch correction, key gene detection, and perturbation prediction. SpaDecoder leverages multiple neighboring spatial or temporal tissue slices, 3D spatial structure, and single-cell reference profiles to deconvolve spatial transcriptomics data and characterize changing tissue composition. LineageMap integrates lineage barcodes, gene expression, and spatial locations to reconstruct high-resolution lineage trees and infer ancestral cell states and locations.

报告人简介：

Xiuwei Zhang is an Associate Professor in the School of Computational Science and Engineering at the Georgia Institute of Technology. Her group develops computational methods for single-cell and spatial transcriptomics, lineage tracing, perturbation analysis, foundation models for biology, network inference, and simulation tools for evaluation of computational methods. She has received the NSF CAREER Award and the NIH MIRA Award.