Reversed graph embedding resolves complex single-cell trajectories

Monocle 2 uses reversed graph embedding to automatically learn complex, branched pseudotime trajectories of differentiation or cellular state changes from single-cell expression data. Monocle 2 uses reversed graph embedding to automatically learn complex, branched pseudotime trajectories of differentiation or cellular state changes from single-cell expression data. Single-cell trajectories can unveil how gene regulation governs cell fate decisions. However, learning the structure of complex trajectories with multiple branches remains a challenging computational problem. We present Monocle 2, an algorithm that uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. We applied Monocle 2 to two studies of blood development and found that mutations in the genes encoding key lineage transcription factors divert cells to alternative fates.