ArticleBMC bioinformatics2004
Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE).
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 33 papers.
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Who cites it
33 citing papers in PubMed.
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- LKB1 and AMPK differentially regulate pancreatic β-cell identity.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2014Article
- Pressure-overload cardiac hypertrophy is associated with distinct alternative splicing due to altered expression of splicing factors.Molecules and cells · 2014Article
- An iteration normalization and test method for differential expression analysis of RNA-seq data.BioData mining · 2014Article
- Assessing the validity and reproducibility of genome-scale predictions.Bioinformatics (Oxford, England) · 2013Article
- Differential expression analysis for paired RNA-Seq data.BMC bioinformatics · 2013Article
- Analysis of the transcriptome of differentiating and non-differentiating preadipocytes from rats and humans by next generation sequencing.Molecular and cellular biochemistry · 2012Article
- Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.Nucleic acids research · 2012Article
- Differential Expression Analysis for RNA-Seq Data.ISRN bioinformatics · 2012Article
- Deep RNA sequencing reveals novel cardiac transcriptomic signatures for physiological and pathological hypertrophy.PloS one · 2012Article
- Next-generation insights into regulatory T cells: expression profiling and FoxP3 occupancy in Human.Nucleic acids research · 2011Article
- Poly (A)+ transcriptome assessment of ERBB2-induced alterations in breast cell lines.PloS one · 2011Article
- Empirical bayes analysis of sequencing-based transcriptional profiling without replicates.BMC bioinformatics · 2010Article
- A survey of statistical software for analysing RNA-seq data.Human genomics · 2010Article
- Tissue-specific transcript annotation and expression profiling with complementary next-generation sequencing technologies.Nucleic acids research · 2010Article
- Bayesian Modeling of MPSS Data: Gene Expression Analysis of Bovine Salmonella Infection.Journal of the American Statistical Association · 2010Article
- Into the unknown: expression profiling without genome sequence information in CHO by next generation sequencing.Nucleic acids research · 2010Article
- Statistical design and analysis of RNA sequencing data.Genetics · 2010Article
- ProbFAST: Probabilistic functional analysis system tool.BMC bioinformatics · 2010Article
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4 authors.
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Abstract
backgroundAn important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error.
resultsWe introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it.
conclusionUsing available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.
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