ArticleBMC bioinformatics2004
Overdispersed logistic regression for SAGE: modelling multiple groups and covariates.
Article in BMC bioinformatics, 2004. 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.
- Exploration of Small Non-Coding RNAs as Molecular Markers of Ram Sperm Fertility.International journal of molecular sciences · 2025Article
- multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments.Bioinformatics (Oxford, England) · 2019Article
- Genome-wide endogenous DAF-16/FOXO recruitment dynamics during lowered insulin signalling in C. elegans.Oncotarget · 2015Article
- Bayesian Hierarchical Model for Differential Gene Expression Using RNA-seq Data.Statistics in biosciences · 2015Article
- Article
- RNA-seq Data: Challenges in and Recommendations for Experimental Design and Analysis.Current protocols in human genetics · 2014Article
- Discriminating metastasised from non-metastasised seminoma based on transcriptional changes in primary tumours using NGS.British journal of cancer · 2014Article
- A Bayesian Semi-parametric Approach for the Differential Analysis of Sequence Counts Data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2014Article
- Small RNAs in the peripheral blood discriminate metastasized from non-metastasized seminoma.Molecular cancer · 2014Article
- Clinically apparent and occult metastasized seminoma: almost indistinguishable on the transcriptional level.PloS one · 2014Article
- Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data.Statistical methods in medical research · 2013Article
- Differential expression analysis for paired RNA-Seq data.BMC bioinformatics · 2013Article
- Statistical methods for identifying differentially expressed genes in RNA-Seq experiments.Cell & bioscience · 2012Article
- Normalization, testing, and false discovery rate estimation for RNA-sequencing data.Biostatistics (Oxford, England) · 2012Article
- Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.Nucleic acids research · 2012Article
- Bayesian hierarchical modeling and selection of differentially expressed genes for the EST data.Biometrics · 2011Article
- Gene expression profiling of human breast tissue samples using SAGE-Seq.Genome research · 2010Article
- A survey of statistical software for analysing RNA-seq data.Human genomics · 2010Article
- Effective population management practices in diabetes care - an observational study.BMC health services research · 2010Article
- baySeq: empirical Bayesian methods for identifying differential expression in sequence count data.BMC bioinformatics · 2010Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundTwo major identifiable sources of variation in data derived from the Serial Analysis of Gene Expression (SAGE) are within-library sampling variability and between-library heterogeneity within a group. Most published methods for identifying differential expression focus on just the sampling variability. In recent work, the problem of assessing differential expression between two groups of SAGE libraries has been addressed by introducing a beta-binomial hierarchical model that explicitly deals with both of the above sources of variation. This model leads to a test statistic analogous to a weighted two-sample t-test. When the number of groups involved is more than two, however, a more general approach is needed.
resultsWe describe how logistic regression with overdispersion supplies this generalization, carrying with it the framework for incorporating other covariates into the model as a byproduct. This approach has the advantage that logistic regression routines are available in several common statistical packages.
conclusionsThe described method provides an easily implemented tool for analyzing SAGE data that correctly handles multiple types of variation and allows for more flexible modelling.
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