Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments

Sartor, Maureen A; Tomlinson, Craig R; Wesselkamper, Scott C; Sivaganesan, Siva; Leikauf, George D; Medvedovic, Mario

doi:10.1186/1471-2105-7-538

Cited by 263Gene expression and cancer classificationBioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

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Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments

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Sartor, Maureen A, Tomlinson, Craig R, Wesselkamper, Scott C, Sivaganesan, Siva, Leikauf, George D, Medvedovic, Mario·2006·BMC Bioinformatics

DOI 10.1186/1471-2105-7-538PMC PMC1781470

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Abstract

BACKGROUND: The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating variability of gene expression measurements in microarray experiments is essential for correctly identifying differentially expressed genes. Several recently developed methods for testing differential expression of genes utilize hierarchical Bayesian models to "pool" information from multiple genes. We have developed a statistical testing procedure that further improves upon current methods by incorporating the well-documented relationship between the absolute gene expression level and the variance of gene expression measurements into the general empirical Bayes framework. RESULTS: We present a novel Bayesian moderated-T, which we show to perform favorably in simulations, with two real, dual-channel microarray experiments and in two controlled single-channel experiments. In simulations, the new method achieved greater power while correctly estimating the true proportion of false positives, and in the analysis of two publicly-available "spike-in" experiments, the new method performed favorably compared to all tested alternatives. We also applied our method to two experimental datasets and discuss the additional biological insights as revealed by our method in contrast to the others. The R-source code for implementing our algorithm is freely available at http://eh3.uc.edu/ibmt. CONCLUSION: We use a Bayesian hierarchical normal model to define a novel Intensity-Based Moderated T-statistic (IBMT). The method is completely data-dependent using empirical Bayes philosophy to estimate hyperparameters, and thus does not require specification of any free parameters. IBMT has the strength of balancing two important factors in the analysis of microarray data: the degree of independence of variances relative to the degree of identity (i.e. t-tests vs. equal variance assumption), and the relationship between variance and signal intensity. When this variance-intensity relationship is weak or does not exist, IBMT reduces to a previously described moderated t-statistic. Furthermore, our method may be directly applied to any array platform and experimental design. Together, these properties show IBMT to be a valuable option in the analysis of virtually any microarray experiment.

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The global variance estimate can now incorporate a mean-variance trend.
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In vitro and in vivo Effects of Lactate on Metabolism and Cytokine Production of Human Primary PBMCs and Monocytes
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Perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), and perfluorononanoic acid (PFNA) increase triglyceride levels and decrease cholesterogenic gene expression in human HepaRG liver cells
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Fibroblast growth factor 21 reflects liver fat accumulation and dysregulation of signalling pathways in the liver of C57BL/6J mice
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BACH1 Is a Specific Repressor of HMOX1 That Is Inactivated by Arsenite
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Cholangiopathy and Biliary Fibrosis in Cyp2c70-Deficient Mice Are Fully Reversed by Ursodeoxycholic Acid
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β2→1-Fructans Modulate the Immune System In Vivo in a Microbiota-Dependent and -Independent Fashion
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Identification of genomic biomarkers for concurrent diagnosis of drug-induced renal tubular injury using a large-scale toxicogenomics database
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Microarray Analysis Reveals Novel Features of the Muscle Aging Process in Men and Women
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Nonpathogenic Lactobacillus rhamnosus activates the inflammasome and antiviral responses in human macrophages
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Resistant Starch Induces Catabolic but Suppresses Immune and Cell Division Pathways and Changes the Microbiome in the Proximal Colon of Male Pigs
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Identification of a mammalian silicon transporter
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Differences in food intake of tumour‐bearing cachectic mice are associated with hypothalamic serotonin signalling
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The whole transcriptome effects of the PPARα agonist fenofibrate on livers of hepatocyte humanized mice
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Molecular profiling of kidney compartments from serial biopsies differentiate treatment responders from non-responders in lupus nephritis
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Reduced NHE3-mediated Na+ absorption increases survival and decreases the incidence of intestinal obstructions in cystic fibrosis mice
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Auxin-dependent control of cytoskeleton and cell shape regulates division orientation in the Arabidopsis embryo
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Chronic hyperglycemia reduces substrate oxidation and impairs metabolic switching of human myotubes
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Characterising the immune profile of the kidney biopsy at lupus nephritis flare differentiates early treatment responders from non-responders
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NF-κB driven cardioprotective gene programs; Hsp70.3 and cardioprotection after late ischemic preconditioning
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Targeted Overexpression of Vav3 Oncogene in Prostatic Epithelium Induces Nonbacterial Prostatitis and Prostate Cancer
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Increased hypothalamic serotonin turnover in inflammation-induced anorexia
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Maternal Western-Style High Fat Diet Induces Sex-Specific Physiological and Molecular Changes in Two-Week-Old Mouse Offspring
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Authors: Sartor, M. A., Tomlinson, C. R., Wesselkamper, S. C., Sivaganesan, S., Leikauf, G. D., & Medvedovic, M.
Affiliations: University of Cincinnati (US)
Dartmouth College (US)
Cincinnati Children's Hospital Medical Center (US)
Corresponding author: Medvedovic, Mario
ORCID: Sartor, Maureen A
Tomlinson, Craig R
Sivaganesan, Siva
Leikauf, George D
Medvedovic, Mario
Venue: BMC Bioinformatics
Publisher: Springer Science and Business Media LLC
Published: 2006-12
Volume: 7
Issue: 1
Pages: 538-538
ISSN: 1471-2105 (ISSN-L)
DOI: 10.1186/1471-2105-7-538
PMC: PMC1781470
OpenAlex: W1570746489
Cited by: 263
References: 62
Citations by year: 2026: 1 · 2024: 1 · 2023: 10 · 2022: 8 · 2021: 14 · 2020: 17 · 2019: 19 · 2018: 21 · 2017: 20 · 2016: 18 · 2015: 14 · 2014: 11 · 2013: 19 · 2012: 16
FWCI: 4.57
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: BMC Bioinformatics (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders: National Institute of Environmental Health Sciences (P30ES006096, U01ES015675)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords: differentially expressed genes, microarray data analysis, Bayesian hierarchical model, empirical Bayes, false discovery rate, spike-in experiments, statistical power
MeSH terms: Animals, Bayes Theorem, Cells, Cultured, Computer Simulation, Female, Models, Genetic, Nickel, Signal Transduction, Transforming Growth Factor beta, Fibroblast Growth Factor 2, Receptors, Aryl Hydrocarbon, Mice, Knockout, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Mice, Aquaporin 5, Basic Helix-Loop-Helix Proteins

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Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments

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