ArticleProceedings of the National Academy of Sciences of the United States of America2022
Correlated gene modules uncovered by high-precision single-cell transcriptomics.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Gene expression of tight junctions in foreskin is not affected by HIV pre-exposure prophylaxis.Frontiers in immunology · 2024Trial
- PSGRN: Gene regulatory network inference from single-cell perturbational data through self-training with synthetic gold standards.Science advances · 2026Article
- Principles of protein abundance regulation across single cells in a mammalian tissue.bioRxiv : the preprint server for biology · 2025Article
- RCANE: a deep learning algorithm for whole-genome pan-cancer somatic copy number aberration prediction using RNA-seq data.Communications biology · 2025Article
- Fluctuation structure predicts genome-wide perturbation outcomes.Research square · 2025Article
- Modeling combinatorial regulation from single-cell multi-omics provides regulatory units underpinning cell type landscape using cRegulon.Genome biology · 2025Article
- Fluctuation structure predicts genome-wide perturbation outcomes.bioRxiv : the preprint server for biology · 2025Article
- Artificial intelligence approaches for tumor phenotype stratification from single-cell transcriptomic data.eLife · 2025Article
- Dynamics of Single-Cell Protein Covariation during Epithelial-Mesenchymal Transition.Journal of proteome research · 2025Article
- Genome-wide single-cell and single-molecule footprinting of transcription factors with deaminase.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Pathway reconstruction and metabolic engineering for the de novo and enhancing production of monacolin J in Pichia pastoris.Bioprocess and biosystems engineering · 2024Article
- ImputeHiFI: An Imputation Method for Multiplexed DNA FISH Data by Utilizing Single-Cell Hi-C and RNA FISH Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Dynamics of single-cell protein covariation during epithelial-mesenchymal transition.bioRxiv : the preprint server for biology · 2024Article
- Beyond single cells: microfluidics empowering multiomics analysis.Analytical and bioanalytical chemistry · 2024Review
- Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes.Nature communications · 2024Article
- Spatial transcriptomic patterns underlying amyloid-β and tau pathology are associated with cognitive dysfunction in Alzheimer's disease.Cell reports · 2024Article
- scHi-CSim: a flexible simulator that generates high-fidelity single-cell Hi-C data for benchmarking.Journal of molecular cell biology · 2023Article
- The pseudogenes of eukaryotic translation elongation factors (EEFs): Role in cancer and other human diseases.Genes & diseases · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Correlations in gene expression are used to infer functional and regulatory relationships between genes. However, correlations are often calculated across different cell types or perturbations, causing genes with unrelated functions to be correlated. Here, we demonstrate that correlated modules can be better captured by measuring correlations of steady-state gene expression fluctuations in single cells. We report a high-precision single-cell RNA-seq method called MALBAC-DT to measure the correlation between any pair of genes in a homogenous cell population. Using this method, we were able to identify numerous cell-type specific and functionally enriched correlated gene modules. We confirmed through knockdown that a module enriched for p53 signaling predicted p53 regulatory targets more accurately than a consensus of ChIP-seq studies and that steady-state correlations were predictive of transcriptome-wide response patterns to perturbations. This approach provides a powerful way to advance our functional understanding of the genome.
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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.