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  3. scFAN: Predicting transcription factor binding in single cells through deep learning

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scFAN: Predicting transcription factor binding in single cells through deep learning

What is being modeled?
Genome-wide binding profiles of transcription factors
Description & purpose of resource

scFAN is a deep learning model that predicts the probability of a TF binding at a given genomic region, with inputs of ATAC-seq, DNA sequence, and DNA mapability data from that region.

Spatial scales
cellular
tissue
organ
Temporal scales
1 - 103 s
hours
days
This resource is currently
mature and useful in ongoing research
Has this resource been validated?
Yes
Link to Resource Credibility Assessment
scFAN: Predicting transcription factor binding in single cells through deep learning
Key publications (e.g. describing or using resource)

Fu, Laiyi, Lihua Zhang, Emmanuel Dollinger, Qinke Peng, Qing Nie, and Xiaohui Xie. "Predicting transcription factor binding in single cells through deep learning." Science Advances 6, no. 51 (2020): eaba9031.

DOI link to publication describing this resource

10.1126/sciadv.aba9031

Link to resource
scFAN package
Collaborators
Xiaohui Xie (PI)
Qing Nie (PI)
PI contact information
xhx@uci.edu; qnie@uci.edu
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