Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics скачать в хорошем качестве

Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics

Cells continually receive and send information, primarily in the form of biochemical signaling due to ligand-receptor binding. From single-cell RNA-sequencing and spatial transcriptomics one can extract high-dimensional gene expression patterns that can be described by intercellular communication networks or decoupled gene modules. These two descriptions of information flow are often assumed to occur independently. However, intercellular communication drives directed flows of information that are mediated by intracellular gene modules, in turn triggering outflows of other signals. We present FlowSig, a method that infers communication-driven intercellular flows from scRNA-seq or ST data using graphical causal modeling and conditional independence. We demonstrate FlowSig’s utility by applying it to various scRNA-seq and spatial transcriptomics studies. Axel Almet is a postdoctoral scholar at the University of California, Irvine and is supervised by Professor Qing Nie (Department of Mathematics). Previously, he earned a Bachelor's and Master's degree at the University of Melbourne, and then a DPhil in Mathematical Biology at the University of Oxford, where he was supervised by Professor Helen Byrne, Professor Philip Maini, and Professor Derek Moulton. He is currently interested in modeling and analyzing the role of cell-cell communication and its effects using high-dimensional sequencing data, particularly in processes such as wound healing and development.