ArticleCell reports2022
Network inference with Granger causality ensembles on single-cell transcriptomics.
Article in Cell reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 1 of them a synthesis that pooled it.
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Who cites it
75 citing papers in PubMed, 1 synthesis or guideline pooled it, 122 citations in OpenAlex.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- CoReGRN: a context-aware post-processing framework for gene regulatory network inference revealing regulatory signatures in HIV-Leishmaniasis.BMC genomics · 2026Article
- From time-course expression to gene regulation: direct linear ODE inference without finite-difference approximation.bioRxiv : the preprint server for biology · 2026Article
- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Shape-constrained, changepoint additive models for time series omics data with cpam.Nucleic acids research · 2026Article
- AutoGERN: single-cell RNA-seq gene regulatory network inference via explicit link modeling and adaptive architectures.Bioinformatics (Oxford, England) · 2026Article
- Federated causal discovery in medicine: trends, opportunities, and challenges.Frontiers in digital health · 2026Article
- Gene regulatory network inference with popInfer reveals the dynamic regulation of hematopoietic stem cell quiescence.iScience · 2025Article
- Inferring the regulation dynamics of oscillatory networks from scRNA-seq data.bioRxiv : the preprint server for biology · 2025Article
- Multi-omic network inference from time-series data.NPJ systems biology and applications · 2025Article
- Improved gene regulatory network inference from single cell data with dropout augmentation.PLoS computational biology · 2025Article
- Unveiling causal regulatory mechanisms through cell-state parallax.Nature communications · 2025Article
- Inferring causal trajectories from spatial transcriptomics using CASCAT.Nucleic acids research · 2025Article
- Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data.Nature communications · 2025Article
- scRDEN: single-cell dynamic gene rank differential expression network and robust trajectory inference.Scientific reports · 2025Article
- scMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data.Nature communications · 2025Article
- GAEDGRN: reconstruction of gene regulatory networks based on gravity-inspired graph autoencoders.Briefings in bioinformatics · 2025Article
- Hypergraph reconstruction from dynamics.Nature communications · 2025Article
- Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders.Briefings in bioinformatics · 2025Article
- EfficientNet-resDDSC: A Hybrid Deep Learning Model Integrating Residual Blocks and Dilated Convolutions for Inferring Gene Causality in Single-Cell Data.Interdisciplinary sciences, computational life sciences · 2025Article
15 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Cellular gene expression changes throughout a dynamic biological process, such as differentiation. Pseudotimes estimate cells' progress along a dynamic process based on their individual gene expression states. Ordering the expression data by pseudotime provides information about the underlying regulator-gene interactions. Because the pseudotime distribution is not uniform, many standard mathematical methods are inapplicable for analyzing the ordered gene expression states. Here we present single-cell inference of networks using Granger ensembles (SINGE), an algorithm for gene regulatory network inference from ordered single-cell gene expression data. SINGE uses kernel-based Granger causality regression to smooth irregular pseudotimes and missing expression values. It aggregates predictions from an ensemble of regression analyses to compile a ranked list of candidate interactions between transcriptional regulators and target genes. In two mouse embryonic stem cell differentiation datasets, SINGE outperforms other contemporary algorithms. However, a more detailed examination reveals caveats about poor performance for individual regulators and uninformative pseudotimes.
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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.