Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.07.12.548785v1?rss=1
Authors: Li, j., Pan, X., Yuan, Y., Shen, H.-B.
Abstract:
RNA velocity is closely related with cell fate and is an important indicator for the prediction of cell states with elegant physical explanation derived from single-cell RNA-seq data. However, most existing RNA velocity models can only be applied to datasets with unspliced/spliced or new/total RNA abundance information. Moreover, the dynamics constructed by these models can not fit the co-expression of spliced and unspliced RNAs well. Motivated by the finding that RNA velocity could be driven by the transcriptional regulation, we propose TFvelo, which expands RNA velocity concept to various single-cell datasets without splicing information, by introducing gene regulatory network information. Our experiments on synthetic data and scRNA-Seq data demonstrate that TFvelo can better model the gene dynamics, infer cell pseudo-time and trajectory, and also detect the key TF-target regulation simultaneously. TFvelo opens a novel, robust and accurate avenue for modeling RNA velocity for single cell data.
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