Unsupervised construction of gene regulatory network based on single-cell multi-omics data of colorectal cancer

Identifying gene regulatory networks (GRNs) at the resolution of single cells has long been a great challenge, and the advent of single-cell multi-omics data provides unprecedented opportunities to construct GRNs. Here, we propose a novel strategy to integrate omics datasets of single-cell ribonucleic acid sequencing and single-cell Assay for Transposase-Accessible Chromatin using sequencing, and using an unsupervised learning neural network to divide the samples with high copy number variation scores, which are used to infer the GRN in each gene block. Accuracy validation of proposed strategy shows that approximately 80% of transcription factors are directly associated with cancer, colorectal cancer, malignancy and disease by TRRUST; and most transcription factors are prone to produce multiple transcript variants and lead to tumorigenesis by RegNetwork database, respectively. The source code access are available at: https://github.com/Cuily-v/Colorectal_cancer.

Automated summary

This study proposes a novel strategy to construct gene regulatory networks (GRNs) at the resolution of single cells. It integrates single-cell RNA sequencing and single-cell Assay for Transposase-Accessible Chromatin using sequencing data and uses an unsupervised learning neural network to identify the GRN in each gene block.