RT Journal Article SR Electronic T1 SLICER: Inferring Branched, Nonlinear Cellular Trajectories from Single Cell RNA-seq Data JF bioRxiv FD Cold Spring Harbor Laboratory SP 047845 DO 10.1101/047845 A1 Joshua D. Welch A1 Alexander Hartemink A1 Jan F. Prins YR 2016 UL http://biorxiv.org/content/early/2016/04/09/047845.abstract AB Single cell experiments provide an unprecedented opportunity to reconstruct a sequence of changes in a biological process from individual “snapshots” of cells. However, nonlinear gene expression changes, genes unrelated to the process, and the possibility of branching trajectories make this a challenging problem. We developed SLICER (Selective Locally Linear Inference of Cellular Expression Relationships) to address these challenges. SLICER can infer highly nonlinear trajectories, select genes without prior knowledge of the process, and automatically determine the location and number of branches and loops. SLICER more accurately recovers the ordering of points along simulated trajectories than existing methods. We demonstrate the effectiveness of SLICER on previously published data from mouse lung cells and neural stem cells.