RT Journal Article SR Electronic T1 CytoTalk: De novo construction of signal transduction networks using single-cell RNA-Seq data JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.03.29.014464 DO 10.1101/2020.03.29.014464 A1 Yuxuan Hu A1 Tao Peng A1 Lin Gao A1 Kai Tan YR 2020 UL http://biorxiv.org/content/early/2020/03/30/2020.03.29.014464.abstract AB 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 a single-cell RNA-Seq data set on mouse visual cortex and evaluated predictions using high-throughput spatial transcriptomics data generated from the same tissue. 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.