- Select a language for the TTS:
- UK English Female
- UK English Male
- US English Female
- US English Male
- Australian Female
- Australian Male
- Language selected: (auto detect) - EN
Play all audios:
You have full access to this article via your institution. Download PDF Trapnell, C. _et al_. _Nat. Biotechnol._ 32, 381–386 (2014). Following gene expression over time is critical to
understanding dynamic cellular processes such as differentiation, and collecting data from single cells has the potential to untangle these phenomena at high resolution. Trapnell _et al_.
introduce Monocle, an unsupervised algorithm that can place single cells in 'pseudotemporal' order on the basis of their expression profiles. The researchers used Monocle to
investigate the differentiation of myoblast progenitors into muscle. They were able to identify key events in differentiation that were not detected by bulk cell sequencing or single-cell
profiles ordered by time of collection. Monocle also detected alternate differentiation trajectories and a group of undifferentiated cells. The increased temporal resolution allowed the
researchers to perform better gene coexpression analysis and identify eight new transcription factors implicated in muscle development. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT
THIS ARTICLE CITE THIS ARTICLE Computing single-cell trajectories. _Nat Methods_ 11, 476 (2014). https://doi.org/10.1038/nmeth.2946 Download citation * Published: 29 April 2014 * Issue Date:
May 2014 * DOI: https://doi.org/10.1038/nmeth.2946 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link
is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative
