Editorial: Unsupervised Learning Models for Unlabeled Genomic, Transcriptomic &amp; Proteomic Data

Xi, Jianing; Yu, Zhenhua

doi:10.3389/fgene.2021.781698

Cited by 2 publications

(1 citation statement)

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“…Comprehensive surveys on multi-modal or multi-view learning applied to multi-omics datasets can be found in. 8,18 The works mentioned above explore gene expression regulatory mechanisms by combining multiple views of the same cell population. This work investigates gene regulatory networks by clustering two diverse entity types together: We sought to group CREs with their correspondent regulated genes.…”

Section: Introductionmentioning

confidence: 99%

DeepReGraph co-clusters temporal gene expression and cis-regulatory elements through heterogeneous graph representation learning

et al. 2022

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This work presents DeepReGraph, a novel method for co-clustering genes and cis-regulatory elements (CREs) into candidate regulatory networks. Gene expression data, as well as data from three CRE activity markers from a publicly available dataset of mouse fetal heart tissue, were used for DeepReGraph concept proofing. In this study we used open chromatin accessibility from ATAC-seq experiments, as well as H3K27ac and H3K27me3 histone marks as CREs activity markers. However, this method can be executed with other sets of markers. We modelled all data sources as a heterogeneous graph and adapted a state-of-the-art representation learning algorithm to produce a low-dimensional and easy-to-cluster embedding of genes and CREs. Deep graph auto-encoders and an adaptive-sparsity generative model are the algorithmic core of DeepReGraph. The main contribution of our work is the design of proper combination rules for the heterogeneous gene expression and CRE activity data and the computational encoding of well-known gene expression regulatory mechanisms into a suitable objective function for graph embedding. We showed that the co-clusters of genes and CREs in the final embedding shed light on developmental regulatory mechanisms in mouse fetal-heart tissue. Such clustering could not be achieved by using only gene expression data. Function enrichment analysis proves that the genes in the co-clusters are involved in distinct biological processes. The enriched transcription factor binding sites in CREs prioritize the candidate transcript factors which drive the temporal changes in gene expression. Consequently, we conclude that DeepReGraph could foster hypothesis-driven tissue development research from high-throughput expression and epigenomic data. Full source code and data are available on the DeepReGraph GitHub project.

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Section: Introductionmentioning

confidence: 99%