Emerging single-cell RNA-seq technologies has made it possible to capture and assess the gene expression of individual cells. Based on the similarity of gene expression profiles, many tools have been developed to generate an in silico ordering of cells in the form of pseudo-time trajectories. However, these tools do not provide a means to find the ordering of critical gene expression changes over pseudo-time.
Duke-NUS Medical School researchers have developed GeneSwitches, a tool that takes any single-cell pseudo-time trajectory and determines the precise order of gene-expression and functional-event changes over time. GeneSwitches uses a statistical framework based on logistic regression to identify the order in which genes are either switched on or off along pseudo-time. With this information, users can identify the order in which surface markers appear, investigate how functional ontologies are gained or lost over time, and compare the ordering of switching genes from two related pseudo-temporal processes.
Plot of selected genes along a pseudo-timeline
Genes that are switched on are plotted above the line, while those switching off are below the line