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PAGE: Parametric Analysis of Gene Set Enrichment

Bibliographic Details
Title: PAGE: Parametric Analysis of Gene Set Enrichment
Authors: Kim, Seon-Young, Volsky, David Julian
Publisher Information: BioMed Central
Publication Year: 2005
Collection: Columbia University: Academic Commons
Subject Terms: Bioinformatics--Methodology, Statistics, DNA microarrays--Data processing, Bioinformatics, Genetics, Biometry
Description: Background: Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate. Results: We developed a modified gene set enrichment analysis method based on a parametric statistical analysis model. Compared with GSEA, the parametric analysis of gene set enrichment (PAGE) detected a larger number of significantly altered gene sets and their p-values were lower than the corresponding p-values calculated by GSEA. Because PAGE uses normal distribution for statistical inference, it requires less computation than GSEA, which needs repeated computation of the permutated data set. PAGE was able to detect significantly changed gene sets from microarray data irrespective of different Affymetrix probe level analysis methods or different microarray platforms. Comparison of two aged muscle microarray data sets at gene set level using PAGE revealed common biological themes better than comparison at individual gene level. Conclusion: PAGE was statistically more sensitive and required much less computational effort than GSEA, it could identify significantly changed biological themes from microarray data irrespective of analysis methods or microarray platforms, and it was useful in comparison of multiple microarray data sets. We offer PAGE as a useful microarray analysis method.
Document Type: article in journal/newspaper
Language: English
Relation: https://doi.org/10.7916/D84X568J
DOI: 10.7916/D84X568J
Availability: https://doi.org/10.7916/D84X568J
Accession Number: edsbas.98A132EE
Database: BASE
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  Data: PAGE: Parametric Analysis of Gene Set Enrichment
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  Data: <searchLink fieldCode="AR" term="%22Kim%2C+Seon-Young%22">Kim, Seon-Young</searchLink><br /><searchLink fieldCode="AR" term="%22Volsky%2C+David+Julian%22">Volsky, David Julian</searchLink>
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  Data: 2005
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  Data: Columbia University: Academic Commons
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  Data: <searchLink fieldCode="DE" term="%22Bioinformatics--Methodology%22">Bioinformatics--Methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22DNA+microarrays--Data+processing%22">DNA microarrays--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Bioinformatics%22">Bioinformatics</searchLink><br /><searchLink fieldCode="DE" term="%22Genetics%22">Genetics</searchLink><br /><searchLink fieldCode="DE" term="%22Biometry%22">Biometry</searchLink>
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  Data: Background: Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate. Results: We developed a modified gene set enrichment analysis method based on a parametric statistical analysis model. Compared with GSEA, the parametric analysis of gene set enrichment (PAGE) detected a larger number of significantly altered gene sets and their p-values were lower than the corresponding p-values calculated by GSEA. Because PAGE uses normal distribution for statistical inference, it requires less computation than GSEA, which needs repeated computation of the permutated data set. PAGE was able to detect significantly changed gene sets from microarray data irrespective of different Affymetrix probe level analysis methods or different microarray platforms. Comparison of two aged muscle microarray data sets at gene set level using PAGE revealed common biological themes better than comparison at individual gene level. Conclusion: PAGE was statistically more sensitive and required much less computational effort than GSEA, it could identify significantly changed biological themes from microarray data irrespective of analysis methods or microarray platforms, and it was useful in comparison of multiple microarray data sets. We offer PAGE as a useful microarray analysis method.
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