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Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

BACKGROUND: An 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...

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Detalles Bibliográficos
Autores principales: Vêncio, Ricardo ZN, Brentani, Helena, Patrão, Diogo FC, Pereira, Carlos AB
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2004
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC517707/
https://www.ncbi.nlm.nih.gov/pubmed/15339345
http://dx.doi.org/10.1186/1471-2105-5-119
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author Vêncio, Ricardo ZN
Brentani, Helena
Patrão, Diogo FC
Pereira, Carlos AB
author_facet Vêncio, Ricardo ZN
Brentani, Helena
Patrão, Diogo FC
Pereira, Carlos AB
author_sort Vêncio, Ricardo ZN
collection PubMed
description BACKGROUND: An 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. RESULTS: We 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. CONCLUSION: Using 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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spelling pubmed-5177072004-09-19 Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE) Vêncio, Ricardo ZN Brentani, Helena Patrão, Diogo FC Pereira, Carlos AB BMC Bioinformatics Methodology Article BACKGROUND: An 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. RESULTS: We 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. CONCLUSION: Using 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. BioMed Central 2004-08-31 /pmc/articles/PMC517707/ /pubmed/15339345 http://dx.doi.org/10.1186/1471-2105-5-119 Text en Copyright © 2004 Vêncio et al; licensee BioMed Central Ltd.
spellingShingle Methodology Article
Vêncio, Ricardo ZN
Brentani, Helena
Patrão, Diogo FC
Pereira, Carlos AB
Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)
title Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)
title_full Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)
title_fullStr Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)
title_full_unstemmed Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)
title_short Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)
title_sort bayesian model accounting for within-class biological variability in serial analysis of gene expression (sage)
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC517707/
https://www.ncbi.nlm.nih.gov/pubmed/15339345
http://dx.doi.org/10.1186/1471-2105-5-119
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