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                CytoTalk: De novo construction of signal transduction networks using single-cell transcriptome data


                主讲人:胡宇轩  西安电子科技大学副教授




                主讲人介绍:胡宇轩,西安电子科技大学华山菁英副教授,西安市重点实验室计□ 算生物信息学研究所教师。主要从事单细胞转录组及空间转录组计算分析,细胞间通信网络建模,基因调控网络模式挖掘及其在组合治疗应用方面的研究╲。成果发表在计算生物学领域著名期刊《Nature Communications》等。博士◥期间在宾夕法尼亚大学/费城儿童医院进行国家公派联合培养,从事耐药性相关计算建模等研究。

                内容介绍:Single-cell technology has opened the door for studying signal transduction in a complex tissue at unprecedented resolution. However, there is a lack of analytical methods for de novo construction of signal transduction pathways using single-cell omics data. Here we present CytoTalk, a computational method for de novo constructing cell type-specific signal transduction networks using single-cell RNA-Seq data. CytoTalk first constructs intracellular and intercellular gene-gene interaction networks using an information-theoretic measure between two cell types. Candidate signal transduction pathways in the integrated network are identified using the prize-collecting Steiner forest algorithm. We applied CytoTalk to single-cell RNA-Seq data sets on mouse visual cortex and olfactory bulb and evaluated predictions using high-throughput spatial transcriptomics data generated from the same tissues. Compared to published methods, genes in our inferred signaling pathways have significantly higher spatial expression correlation only in cells that are spatially closer to each other, suggesting improved accuracy of CytoTalk. Furthermore, using single-cell RNA-Seq data with receptor gene perturbation, we found that predicted pathways are enriched for differentially expressed genes between the receptor knockout and wild type cells, further validating the accuracy of CytoTalk. In summary, CytoTalk enables de novo construction of signal transduction pathways and facilitates comparative analysis of these pathways across tissues and conditions.