ArticleBMC bioinformatics2005
Identifying differential expression in multiple SAGE libraries: an overdispersed log-linear model approach.
Article in BMC bioinformatics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
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- CAMDA 2023: Finding patterns in urban microbiomes.Frontiers in genetics · 2024Article
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- Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data.PloS one · 2020Article
- multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments.Bioinformatics (Oxford, England) · 2019Article
- MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks.BMC bioinformatics · 2019Article
- JUM is a computational method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patterns.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- A subset of chemosensory genes differs between two populations of a specialized leaf beetle after host plant shift.Ecology and evolution · 2018Article
- Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics.PLoS biology · 2018Article
- Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq.Nucleic acids research · 2017Article
- SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence.Nucleic acids research · 2016Article
- Confident difference criterion: a new Bayesian differentially expressed gene selection algorithm with applications.BMC bioinformatics · 2015Article
- RNA-Seq: Improving Our Understanding of Retinal Biology and Disease.Cold Spring Harbor perspectives in medicine · 2015Review
- DGEclust: differential expression analysis of clustered count data.Genome biology · 2015Article
- The importance of study design for detecting differentially abundant features in high-throughput experiments.Genome biology · 2014Article
- RNA-seq Data: Challenges in and Recommendations for Experimental Design and Analysis.Current protocols in human genetics · 2014Article
- Waste not, want not: why rarefying microbiome data is inadmissible.PLoS computational biology · 2014Article
- The analytical landscape of static and temporal dynamics in transcriptome data.Frontiers in genetics · 2014Review
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3 authors.
Funding
Abstract
backgroundIn testing for differential gene expression involving multiple serial analysis of gene expression (SAGE) libraries, it is critical to account for both between and within library variation. Several methods have been proposed, including the t test, tw test, and an overdispersed logistic regression approach. The merits of these tests, however, have not been fully evaluated. Questions still remain on whether further improvements can be made.
resultsIn this article, we introduce an overdispersed log-linear model approach to analyzing SAGE; we evaluate and compare its performance with three other tests: the two-sample t test, tw test and another based on overdispersed logistic linear regression. Analysis of simulated and real datasets show that both the log-linear and logistic overdispersion methods generally perform better than the t and tw tests; the log-linear method is further found to have better performance than the logistic method, showing equal or higher statistical power over a range of parameter values and with different data distributions.
conclusionOverdispersed log-linear models provide an attractive and reliable framework for analyzing SAGE experiments involving multiple libraries. For convenience, the implementation of this method is available through a user-friendly web-interface available at http://www.cbcb.duke.edu/sage.
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