ArticleGenomics2011
Capturing changes in gene expression dynamics by gene set differential coordination analysis.
Article in Genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Alzheimer's Disease Risk AllelebioRxiv : the preprint server for biology · 2026Article
- Analysis of individual patient pathway coordination in a cross-species single-cell kidney atlas.Nature genetics · 2025Article
- Cardiovascular Risk and Resilience Among Black Adults: Rationale and Design of the MECA Study.Journal of the American Heart Association · 2020Article
- Understanding sequencing data as compositions: an outlook and review.Bioinformatics (Oxford, England) · 2018Review
- GSAR: Bioconductor package for Gene Set analysis in R.BMC bioinformatics · 2017Article
- K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data.BioMed research international · 2015Article
- MeDiA: Mean Distance Association and Its Applications in Nonlinear Gene Set Analysis.PloS one · 2015Article
- Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets.Bioinformatics (Oxford, England) · 2014Article
- Analyzing LC/MS metabolic profiling data in the context of existing metabolic networks.Current Metabolomics · 2013Article
- Improving gene expression data interpretation by finding latent factors that co-regulate gene modules with clinical factors.BMC genomics · 2011Article
- Hierarchical clustering of high-throughput expression data based on general dependences.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Analyzing gene expression data at the gene set level greatly improves feature extraction and data interpretation. Currently most efforts in gene set analysis are focused on differential expression analysis--finding gene sets whose genes show first-order relationship with the clinical outcome. However the regulation of the biological system is complex, and much of the change in gene expression dynamics do not manifest in the form of differential expression. At the gene set level, capturing the change in expression dynamics is difficult due to the complexity and heterogeneity of the gene sets. Here we report a systematic approach to detect gene sets that show differential coordination patterns with the rest of the transcriptome, as well as pairs of gene sets that are differentially coordinated with each other. We demonstrate that the method can identify biologically relevant gene sets, many of which do not show first-order relationship with the clinical outcome.
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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.