ArticleBiometrics2012
An empirical Bayesian approach for identifying differential coexpression in high-throughput experiments.
Article in Biometrics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Interpretable AI for inference of causal molecular relationships from omics data.Science advances · 2025Article
- A dynamic co-expression approach reveals Gins2 as a potential upstream modulator of HNSCC metastasis.Scientific reports · 2025Article
- Patient-specific analysis of co-expression to measure biological network rewiring in individuals.Life science alliance · 2024Article
- Inference of differential gene regulatory networks using boosted differential trees.Bioinformatics advances · 2024Article
- Limitation of permutation-based differential correlation analysis.Genetic epidemiology · 2023Article
- A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification.Bioinformatics advances · 2023Article
- Modeling dynamic correlation in zero-inflated bivariate count data with applications to single-cell RNA sequencing data.Biometrics · 2022Article
- Network-based Transcriptome-wide Expression Study for Postmenopausal Osteoporosis.The Journal of clinical endocrinology and metabolism · 2020Article
- Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer.Genome biology · 2019Article
- Crosstalk analysis of dysregulated pathways in preeclampsia.Experimental and therapeutic medicine · 2019Article
- Article
- An empirical Bayes approach for multiple tissue eQTL analysis.Biostatistics (Oxford, England) · 2018Article
- A novel approach to select differential pathways associated with hypertrophic cardiomyopathy based on gene co‑expression analysis.Molecular medicine reports · 2017Article
- A Novel Method to Identify Differential Pathways in Hippocampus Alzheimer's Disease.Medical science monitor : international medical journal of experimental and clinical research · 2017Article
- Benchmarking pathway interaction network for colorectal cancer to identify dysregulated pathways.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2017Article
- Differential correlation for sequencing data.BMC research notes · 2017Article
- Pathway Interaction Network Analysis Identifies Dysregulated Pathways in Human Monocytes Infected byComputational and mathematical methods in medicine · 2017Article
- Construction of protein interaction network involved in lung adenocarcinomas using a novel algorithm.Oncology letters · 2016Article
- Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm.Experimental and therapeutic medicine · 2016Article
- The discordant method: a novel approach for differential correlation.Bioinformatics (Oxford, England) · 2016Article
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2 authors.
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Abstract
A common goal of microarray and related high-throughput genomic experiments is to identify genes that vary across biological condition. Most often this is accomplished by identifying genes with changes in mean expression level, so called differentially expressed (DE) genes, and a number of effective methods for identifying DE genes have been developed. Although useful, these approaches do not accommodate other types of differential regulation. An important example concerns differential coexpression (DC). Investigations of this class of genes are hampered by the large cardinality of the space to be interrogated as well as by influential outliers. As a result, existing DC approaches are often underpowered, exceedingly prone to false discoveries, and/or computationally intractable for even a moderately large number of pairs. To address this, an empirical Bayesian approach for identifying DC gene pairs is developed. The approach provides a false discovery rate controlled list of significant DC gene pairs without sacrificing power. It is applicable within a single study as well as across multiple studies. Computations are greatly facilitated by a modification to the expectation-maximization algorithm and a procedural heuristic. Simulations suggest that the proposed approach outperforms existing methods in far less computational time; and case study results suggest that the approach will likely prove to be a useful complement to current DE methods in high-throughput genomic studies.
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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.