ArticleLife science alliance2020
Predicting gene regulatory networks from cell atlases.
Article in Life science alliance, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- A DSSM network for inferring and prioritizing cell-type-specific regulons using single-cell RNA-seq data.BMC bioinformatics · 2025Article
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- Gene regulatory networks in disease and ageing.Nature reviews. Nephrology · 2024Review
- Comprehensive characterization of IFNγ signaling in acute myeloid leukemia reveals prognostic and therapeutic strategies.Nature communications · 2024Article
- Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis.BMC bioinformatics · 2023Article
- Dynamics of hepatocyte-cholangiocyte cell-fate decisions during liver development and regeneration.iScience · 2022Article
- Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome.Frontiers in immunology · 2022Article
- TENET: gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data.Nucleic acids research · 2021Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent single-cell RNA-sequencing atlases have surveyed and identified major cell types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from three major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences, including sampled tissues, sequencing depth, and author assigned cell type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell types from specialised cell type-specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wild-type and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.