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BAMarray™: Java software for Bayesian analysis of variance for microarray data

BACKGROUND: DNA microarrays open up a new horizon for studying the genetic determinants of disease. The high throughput nature of these arrays creates an enormous wealth of information, but also poses a challenge to data analysis. Inferential problems become even more pronounced as experimental desi...

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Autores principales: Ishwaran, Hemant, Rao, J Sunil, Kogalur, Udaya B
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1382258/
https://www.ncbi.nlm.nih.gov/pubmed/16466568
http://dx.doi.org/10.1186/1471-2105-7-59
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author Ishwaran, Hemant
Rao, J Sunil
Kogalur, Udaya B
author_facet Ishwaran, Hemant
Rao, J Sunil
Kogalur, Udaya B
author_sort Ishwaran, Hemant
collection PubMed
description BACKGROUND: DNA microarrays open up a new horizon for studying the genetic determinants of disease. The high throughput nature of these arrays creates an enormous wealth of information, but also poses a challenge to data analysis. Inferential problems become even more pronounced as experimental designs used to collect data become more complex. An important example is multigroup data collected over different experimental groups, such as data collected from distinct stages of a disease process. We have developed a method specifically addressing these issues termed Bayesian ANOVA for microarrays (BAM). The BAM approach uses a special inferential regularization known as spike-and-slab shrinkage that provides an optimal balance between total false detections and total false non-detections. This translates into more reproducible differential calls. Spike and slab shrinkage is a form of regularization achieved by using information across all genes and groups simultaneously. RESULTS: BAMarray™ is a graphically oriented Java-based software package that implements the BAM method for detecting differentially expressing genes in multigroup microarray experiments (up to 256 experimental groups can be analyzed). Drop-down menus allow the user to easily select between different models and to choose various run options. BAMarray™ can also be operated in a fully automated mode with preselected run options. Tuning parameters have been preset at theoretically optimal values freeing the user from such specifications. BAMarray™ provides estimates for gene differential effects and automatically estimates data adaptive, optimal cutoff values for classifying genes into biological patterns of differential activity across experimental groups. A graphical suite is a core feature of the product and includes diagnostic plots for assessing model assumptions and interactive plots that enable tracking of prespecified gene lists to study such things as biological pathway perturbations. The user can zoom in and lasso genes of interest that can then be saved for downstream analyses. CONCLUSION: BAMarray™ is user friendly platform independent software that effectively and efficiently implements the BAM methodology. Classifying patterns of differential activity is greatly facilitated by a data adaptive cutoff rule and a graphical suite. BAMarray™ is licensed software freely available to academic institutions. More information can be found at .
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spelling pubmed-13822582006-02-25 BAMarray™: Java software for Bayesian analysis of variance for microarray data Ishwaran, Hemant Rao, J Sunil Kogalur, Udaya B BMC Bioinformatics Software BACKGROUND: DNA microarrays open up a new horizon for studying the genetic determinants of disease. The high throughput nature of these arrays creates an enormous wealth of information, but also poses a challenge to data analysis. Inferential problems become even more pronounced as experimental designs used to collect data become more complex. An important example is multigroup data collected over different experimental groups, such as data collected from distinct stages of a disease process. We have developed a method specifically addressing these issues termed Bayesian ANOVA for microarrays (BAM). The BAM approach uses a special inferential regularization known as spike-and-slab shrinkage that provides an optimal balance between total false detections and total false non-detections. This translates into more reproducible differential calls. Spike and slab shrinkage is a form of regularization achieved by using information across all genes and groups simultaneously. RESULTS: BAMarray™ is a graphically oriented Java-based software package that implements the BAM method for detecting differentially expressing genes in multigroup microarray experiments (up to 256 experimental groups can be analyzed). Drop-down menus allow the user to easily select between different models and to choose various run options. BAMarray™ can also be operated in a fully automated mode with preselected run options. Tuning parameters have been preset at theoretically optimal values freeing the user from such specifications. BAMarray™ provides estimates for gene differential effects and automatically estimates data adaptive, optimal cutoff values for classifying genes into biological patterns of differential activity across experimental groups. A graphical suite is a core feature of the product and includes diagnostic plots for assessing model assumptions and interactive plots that enable tracking of prespecified gene lists to study such things as biological pathway perturbations. The user can zoom in and lasso genes of interest that can then be saved for downstream analyses. CONCLUSION: BAMarray™ is user friendly platform independent software that effectively and efficiently implements the BAM methodology. Classifying patterns of differential activity is greatly facilitated by a data adaptive cutoff rule and a graphical suite. BAMarray™ is licensed software freely available to academic institutions. More information can be found at . BioMed Central 2006-02-08 /pmc/articles/PMC1382258/ /pubmed/16466568 http://dx.doi.org/10.1186/1471-2105-7-59 Text en Copyright © 2006 Ishwaran et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software
Ishwaran, Hemant
Rao, J Sunil
Kogalur, Udaya B
BAMarray™: Java software for Bayesian analysis of variance for microarray data
title BAMarray™: Java software for Bayesian analysis of variance for microarray data
title_full BAMarray™: Java software for Bayesian analysis of variance for microarray data
title_fullStr BAMarray™: Java software for Bayesian analysis of variance for microarray data
title_full_unstemmed BAMarray™: Java software for Bayesian analysis of variance for microarray data
title_short BAMarray™: Java software for Bayesian analysis of variance for microarray data
title_sort bamarray™: java software for bayesian analysis of variance for microarray data
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1382258/
https://www.ncbi.nlm.nih.gov/pubmed/16466568
http://dx.doi.org/10.1186/1471-2105-7-59
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