Reversed graph embedding resolves complex single-cell trajectories
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.
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.
Abstract
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.
nature.com