ArticleCell systems2017
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures.
Article in Cell systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 314 papers, 1 of them a synthesis that pooled it.
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Who cites it
314 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Joint inference of paired dynamical gene regulatory networks reveals distinct cell-state landscapes of neutrophil reprogramming.bioRxiv : the preprint server for biology · 2026Article
- Sampling bias corrections for discrete and Gaussian partial information decompositions.Patterns (New York, N.Y.) · 2026Article
- Benchmarking methods for inferring single-cell transcription factor activity using large-scale perturbation sequencing data.Briefings in bioinformatics · 2026Article
- Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.Molecular systems biology · 2026Article
- Multiscale learning of gene network-driven phenotypic dynamics of single cells.Molecular systems biology · 2026Article
- FERRET: Framework to Evaluate Robustness in Regulatory Networks Using Heterogeneous Cell Types.bioRxiv : the preprint server for biology · 2026Article
- Tensor network-based gene regulatory network inference for single-cell transcriptomic data.iScience · 2026Article
- A comprehensive survey on graph neural networks for gene regulatory network inference.Briefings in bioinformatics · 2026Review
- CAM-Net: a context-aware network for identifying reliable microbial relations via optimal consortium.Briefings in bioinformatics · 2026Article
- DNA Methylation Stochasticity Is Linked to Transcriptional Variability and Convergent Epigenetic Disruption across Genetic Subtypes of Acute Myeloid Leukemia.Cancer research · 2026Article
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- Inferring cell-specific gene regulatory networks based on causal graph embedding.Cell reports methods · 2026Article
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- DataXflowGen for GenAI-driven model generation.Scientific reports · 2026Article
- Inference of spatial chromatin accessibility via integration of spatial transcriptomics and single-cell multi-omics data.Nature communications · 2026Article
- CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data.Bioinformatics (Oxford, England) · 2026Article
- Developmental synergistic information.Theory in biosciences = Theorie in den Biowissenschaften · 2026Article
- Systematic design of combination therapy by targeting master regulators of coexisting diffuse midline glioma cell states.Nature genetics · 2026Article
- The Cartesian Gaussian additive noise model for directed network inference in omics data.BMC medical research methodology · 2026Article
254 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
While single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher-order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell datasets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.
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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.