ArticlePloS one2012
A Bayesian model for pooling gene expression studies that incorporates co-regulation information.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 12 citations in OpenAlex.
- The prognostic and clinical significance of IFI44L aberrant downregulation in patients with oral squamous cell carcinoma.BMC cancer · 2021Article
- RNA helicase-regulated processing of theThe Journal of biological chemistry · 2020Article
- Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings.International journal of biological sciences · 2018Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
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Abstract
Current Bayesian microarray models that pool multiple studies assume gene expression is independent of other genes. However, in prokaryotic organisms, genes are arranged in units that are co-regulated (called operons). Here, we introduce a new Bayesian model for pooling gene expression studies that incorporates operon information into the model. Our Bayesian model borrows information from other genes within the same operon to improve estimation of gene expression. The model produces the gene-specific posterior probability of differential expression, which is the basis for inference. We found in simulations and in biological studies that incorporating co-regulation information improves upon the independence model. We assume that each study contains two experimental conditions: a treatment and control. We note that there exist environmental conditions for which genes that are supposed to be transcribed together lose their operon structure, and that our model is best carried out for known operon structures.
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