SPARK: Bayesian structure learning of enhancer-based gene regulatory networks from single-cell multiomics data and prior knowledge
- 1Imperial College London
Abstract
Enhancer-based gene regulatory networks (eGRNs) linking enhancers with recruited transcription factors (TFs) and downstream target genes can help decipher complex regulatory mechanisms, such as the function of GWAS variants at enhancers. Molecular assays that profile gene expression and chromatin state jointly from the same cells (such as 10x Multiome) are a rich source of input data for eGRN reconstruction. However, these assays lack mechanistic evidence for enhancer-gene and TF-enhancer links, which can be contributed by prior knowledge from other assays and analyses. Here, we present SPARK, an eGRN reconstruction framework that formally combines single-cell multiome data with mechanistic prior knowledge. SPARK represents eGRNs as directed acyclic graphs (DAGs) and learns them using Bayesian inference, with a prior on the network structure constructed from heterogeneous sources of evidence. In a simulation study, we show that SPARK benefits from prior knowledge and outperforms existing DAG-based and regression-based algorithms. Finally, we use SPARK to study the regulatory circuitry of enhancers harboring immune disease GWAS variants in human primary monocytes, revealing known and novel regulatory links as candidates for experimental validation.
Related
- Interpretable Multiomics Machine Learning Identifies GSDMB-Associated Epigenetic Repression and Reduced Immune Activity in Metastatic Colorectal Cancer
- Neural dynamics of reconstructing knowledge structure from temporally disordered inputs
- Knowledge Structure and Evolution of Global Ancient Tree Research: A Visual Exploration Based on CiteSpace
- Global knowledge structure and thematic evolution in inflammation-associated gastric cancer epigenetics: a multidatabase science-mapping study with nested latent Dirichlet allocation and independent NMF validation
- Microbial community structure of cigar tobacco leaves in Hunan production area and its regulatory mechanism on quality formation
BibTeX
@misc{masaki2026spark,
title = {{SPARK: Bayesian structure learning of enhancer-based gene regulatory networks from single-cell multiomics data and prior knowledge}},
author = {N. Masaki and S. Acharya and V. Malysheva and S. Koturan and L. Bottolo and M. Spivakov},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.22.761021},
url = {https://www.biorxiv.org/content/10.64898/2026.10.22.761021v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - SPARK: Bayesian structure learning of enhancer-based gene regulatory networks from single-cell multiomics data and prior knowledge
AU - N. Masaki
AU - S. Acharya
AU - V. Malysheva
AU - S. Koturan
AU - L. Bottolo
AU - M. Spivakov
PY - 2026
DA - 2026/10/22
PB - bioRxiv
DO - 10.64898/2026.10.22.761021
UR - https://www.biorxiv.org/content/10.64898/2026.10.22.761021v1
AB - Enhancer-based gene regulatory networks (eGRNs) linking enhancers with recruited transcription factors (TFs) and downstream target genes can help decipher complex regulatory mechanisms, such as the function of GWAS variants at enhancers. Molecular assays that profile gene expression and chromatin state jointly from the same cells (such as 10x Multiome) are a rich source of input data for eGRN reconstruction. However, these assays lack mechanistic evidence for enhancer-gene and TF-enhancer links, which can be contributed by prior knowledge from other assays and analyses. Here, we present SPARK, an eGRN reconstruction framework that formally combines single-cell multiome data with mechanistic prior knowledge. SPARK represents eGRNs as directed acyclic graphs (DAGs) and learns them using Bayesian inference, with a prior on the network structure constructed from heterogeneous sources of evidence. In a simulation study, we show that SPARK benefits from prior knowledge and outperforms existing DAG-based and regression-based algorithms. Finally, we use SPARK to study the regulatory circuitry of enhancers harboring immune disease GWAS variants in human primary monocytes, revealing known and novel regulatory links as candidates for experimental validation.
ER -