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Comparison of lists of genes based on functional profiles

BACKGROUND: How to compare studies on the basis of their biological significance is a problem of central importance in high-throughput genomics. Many methods for performing such comparisons are based on the information in databases of functional annotation, such as those that form the Gene Ontology...

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Autores principales: Salicrú, Miquel, Ocaña, Jordi, Sánchez-Pla, Alex
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3747174/
https://www.ncbi.nlm.nih.gov/pubmed/21999355
http://dx.doi.org/10.1186/1471-2105-12-401
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author Salicrú, Miquel
Ocaña, Jordi
Sánchez-Pla, Alex
author_facet Salicrú, Miquel
Ocaña, Jordi
Sánchez-Pla, Alex
author_sort Salicrú, Miquel
collection PubMed
description BACKGROUND: How to compare studies on the basis of their biological significance is a problem of central importance in high-throughput genomics. Many methods for performing such comparisons are based on the information in databases of functional annotation, such as those that form the Gene Ontology (GO). Typically, they consist of analyzing gene annotation frequencies in some pre-specified GO classes, in a class-by-class way, followed by p-value adjustment for multiple testing. Enrichment analysis, where a list of genes is compared against a wider universe of genes, is the most common example. RESULTS: A new global testing procedure and a method incorporating it are presented. Instead of testing separately for each GO class, a single global test for all classes under consideration is performed. The test is based on the distance between the functional profiles, defined as the joint frequencies of annotation in a given set of GO classes. These classes may be chosen at one or more GO levels. The new global test is more powerful and accurate with respect to type I errors than the usual class-by-class approach. When applied to some real datasets, the results suggest that the method may also provide useful information that complements the tests performed using a class-by-class approach if gene counts are sparse in some classes. An R library, goProfiles, implements these methods and is available from Bioconductor, http://bioconductor.org/packages/release/bioc/html/goProfiles.html. CONCLUSIONS: The method provides an inferential basis for deciding whether two lists are functionally different. For global comparisons it is preferable to the global chi-square test of homogeneity. Furthermore, it may provide additional information if used in conjunction with class-by-class methods.
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spelling pubmed-37471742013-08-20 Comparison of lists of genes based on functional profiles Salicrú, Miquel Ocaña, Jordi Sánchez-Pla, Alex BMC Bioinformatics Research Article BACKGROUND: How to compare studies on the basis of their biological significance is a problem of central importance in high-throughput genomics. Many methods for performing such comparisons are based on the information in databases of functional annotation, such as those that form the Gene Ontology (GO). Typically, they consist of analyzing gene annotation frequencies in some pre-specified GO classes, in a class-by-class way, followed by p-value adjustment for multiple testing. Enrichment analysis, where a list of genes is compared against a wider universe of genes, is the most common example. RESULTS: A new global testing procedure and a method incorporating it are presented. Instead of testing separately for each GO class, a single global test for all classes under consideration is performed. The test is based on the distance between the functional profiles, defined as the joint frequencies of annotation in a given set of GO classes. These classes may be chosen at one or more GO levels. The new global test is more powerful and accurate with respect to type I errors than the usual class-by-class approach. When applied to some real datasets, the results suggest that the method may also provide useful information that complements the tests performed using a class-by-class approach if gene counts are sparse in some classes. An R library, goProfiles, implements these methods and is available from Bioconductor, http://bioconductor.org/packages/release/bioc/html/goProfiles.html. CONCLUSIONS: The method provides an inferential basis for deciding whether two lists are functionally different. For global comparisons it is preferable to the global chi-square test of homogeneity. Furthermore, it may provide additional information if used in conjunction with class-by-class methods. BioMed Central 2011-10-16 /pmc/articles/PMC3747174/ /pubmed/21999355 http://dx.doi.org/10.1186/1471-2105-12-401 Text en Copyright ©2011 Salicrú 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 Research Article
Salicrú, Miquel
Ocaña, Jordi
Sánchez-Pla, Alex
Comparison of lists of genes based on functional profiles
title Comparison of lists of genes based on functional profiles
title_full Comparison of lists of genes based on functional profiles
title_fullStr Comparison of lists of genes based on functional profiles
title_full_unstemmed Comparison of lists of genes based on functional profiles
title_short Comparison of lists of genes based on functional profiles
title_sort comparison of lists of genes based on functional profiles
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3747174/
https://www.ncbi.nlm.nih.gov/pubmed/21999355
http://dx.doi.org/10.1186/1471-2105-12-401
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