Salon 2
LEAP: A pseudotime-series based analysis for single-cell sequencing data reveals directed associations between gene expression data (303225)
Jun Li, University of Notre Dame*Alicia Taylor Specht, University of Notre Dame
Keywords: single-cell sequencing, time-series analysis, co-expression network, Pearson’s correlation coefficient
Gene co-expression networks are widely used to understand gene regulations and infer gene functions, but only capture genes that express simultaneously thereby failing to describe the directed associations between genes, which are of great scientific interest. Recent advances in single-cell sequencing technology allow for the sequencing of cells individually and several algorithms now exist that can sort these cells into their pseudotemporal ordering based on gene expression. By ordering single-cell sequencing data based on sets of genes that have experimentally validated involvement in cell cycle processes, we create a pseudotime-series out of the expression profiles of individual cells. We present LEAP, a Lag-based Expression Association for Pseudotime-series. We find significant evidence of directed associations within expression profiles from mouse samples that would be missed by traditional co-expression techniques. We confirm those associations within experimentally validated networks, as well as identify new directed associations for experimental exploration.