Understanding how gene expression in single cells progresses over time is vital for revealing the mechanisms governing cell fate transitions. RNA velocity, which infers immediate changes in gene expression by comparing levels of new (unspliced) versus mature (spliced) transcripts, represents an important advance to these efforts. A key question remaining is whether it is possible to predict the most probable cell state backward or forward over arbitrary time scales.
In this webinar, our speaker, Xiaojie Qiu of the University of California, San Francisco, will share an inclusive model, termed Dynamo, capable of predicting cell states over extended time periods. The model incorporates promoter state switching, transcription, splicing, translation, and RNA/protein degradation by taking advantage of single-cell RNA-seq data and transcriptome/proteome co-assay measurements.
Dr. Qiu will demonstrate how the Dyamo model can be used to infer the entire kinetic behavior of a cell and will show that it is possible to analytically reconstruct the transcriptomic vector field from sparse and noisy vector samples generated by single-cell experiments, especially those produced from metabolic labeling based scRNA-seq (i.e scSLAM-seq, NASC-seq, sci-fate or scNT-seq).
In this webinar you will learn:
- How to use Dynamo to perform RNA velocity analysis and vector field reconstruction
- Details of an inclusive model capable of predicting cell states over extended time periods
- How to analytically reconstruct the transcriptomic vector field from sparse and noisy vector samples generated by single-cell experiments