RT Journal Article
SR Electronic
T1 scDirect: key transcription factor identification for directing cell state transitions based on single-cell multi-omics data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2024.01.08.574757
DO 10.1101/2024.01.08.574757
A1 Li, Chen
A1 Chen, Sijie
A1 Chen, Yixin
A1 Bian, Haiyang
A1 Hao, Minsheng
A1 Wei, Lei
A1 Zhang, Xuegong
YR 2024
UL http://biorxiv.org/content/early/2024/02/09/2024.01.08.574757.abstract
AB Cell state transitions are complicated processes that occur in various life activities. Understanding and artificially manipulating them have been longstanding challenges. Substantial experiments reveal that the transitions could be directed by several key transcription factors (TFs). Here we present scDirect, a computational framework to identify key TFs based on single-cell RNA-seq and ATAC-seq data. scDirect models the TF identification task as a linear inverse problem, and solve it with gene regulatory networks enhanced by a graph attention network. Through a benchmarking on a single-cell human embryonic stem cell atlas, we demonstrate the robustness and superiority of scDirect against alternative analysis methods on TF identification. We apply scDirect on various datasets, and scDirect exhibits high capability in identifying key TFs in cell differentiation and somatic cell conversion. Furthermore, scDirect can efficiently identify TF combinations for cell reprogramming, many of which have been experimentally validated. We envision that scDirect can utilize rapidly increasing single-cell datasets to identify key TFs for directing cell state transitions and may become an effective tool to facilitate cell engineering and regenerative medicine.Competing Interest StatementTsinghua University is applying for a patent for the method of scDirect.