RT Journal Article
SR Electronic
T1 dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using single cell time-course gene expression data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.08.28.458048
DO 10.1101/2021.08.28.458048
A1 Xu, Yu
A1 Chen, Jiaxing
A1 Lyu, Aiping
A1 Cheung, William K
A1 Zhang, Lu
YR 2021
UL http://biorxiv.org/content/early/2021/08/30/2021.08.28.458048.abstract
AB Time-course single-cell RNA sequencing (scRNA-seq) data have been widely applied to reconstruct the cell-type-specific gene regulatory networks by exploring the dynamic changes of gene expression between transcription factors (TFs) and their target genes. The existing algorithms were commonly designed to analyze bulk gene expression data and could not deal with the dropouts and cell heterogeneity in scRNA-seq data. In this paper, we developed dynDeepDRIM that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. dynDeepDRIM integrated the primary image, neighbor images with time-course into a four-dimensional tensor and trained a convolutional neural network to predict the direct regulatory interactions between TFs and genes. We evaluated the performance of dynDeepDRIM on five time-course gene expression datasets. dynDeepDRIM outperformed the state-of-the-art methods for predicting TF-gene direct interactions and gene functions. We also observed gene functions could be better performed if more neighbor images were involved.Competing Interest StatementThe authors have declared no competing interest.