ArticleCell systems2024
Transcriptome data are insufficient to control false discoveries in regulatory network inference.
Article in Cell systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- DataXflowGen for GenAI-driven model generation.Scientific reports · 2026Article
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- T-ChroNet: Time-aware chromatin network reconstruction to detect dynamic regulatory programs in longitudinal epigenetic dataset.NAR genomics and bioinformatics · 2026Article
- Deciphering Cell-Type-Specific Transcriptional Regulation in Tomato Leaves Through Ensemble Machine Learning and Single-Cell Transcriptomics.Plants (Basel, Switzerland) · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Article
- Integration of high-throughput proteomic data and complementary omics layers with PriOmics.Genome research · 2026Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- Gene regulatory network inference with popInfer reveals the dynamic regulation of hematopoietic stem cell quiescence.iScience · 2025Article
- Distilling Direct Effects via Conditional Differential Gene Expression Analysis.bioRxiv : the preprint server for biology · 2025Article
- Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene coexpression networks.Genome research · 2025Article
- Interpretable AI for inference of causal molecular relationships from omics data.Science advances · 2025Article
- Causal modeling of gene effects from regulators to programs to traits: integration of genetic associations and Perturb-seq.bioRxiv : the preprint server for biology · 2025Article
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4 authors.
Funding
Abstract
Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.
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