RT Journal Article SR Electronic T1 Ouija: Incorporating prior knowledge in single-cell trajectory learning using Bayesian nonlinear factor analysis JF bioRxiv FD Cold Spring Harbor Laboratory SP 060442 DO 10.1101/060442 A1 Kieran Campbell A1 Christopher Yau YR 2016 UL http://biorxiv.org/content/early/2016/06/23/060442.abstract AB Pseudotime estimation algorithms from single cell molecular profiling allows the recovery of temporal information from otherwise static profiles of individual cells. This pseudotemporal information can be used to characterise transient events in temporally evolving biological systems. Conventional algorithms typically employ an unsupervised approach that do not model any explicit gene behaviours making them hard to apply in the context of strong prior knowledge. Our approach Ouija takes an alternate approach to pseudotime by explicitly focusing on switch-like marker genes that are ordinarily used to confirm the accuracy of unsupervised pseudotime algorithms. Instead of using the marker genes for confirmation, we derive pseudotimes directly from the marker genes. We show that in many instances a small panel of marker genes can recover pseudotimes that are consistent with those obtained using the whole transcriptome. Ouija therefore provides a powerful complimentary approach to existing approaches to pseudotime estimation.