Down-weighting overlapping genes improves gene set analysis
<p>Abstract</p> <p>Background</p> <p>The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how spec...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2012-06-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://www.biomedcentral.com/1471-2105/13/136 |
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| author | Tarca Adi Draghici Sorin Bhatti Gaurav Romero Roberto |
| author_facet | Tarca Adi Draghici Sorin Bhatti Gaurav Romero Roberto |
| author_sort | Tarca Adi |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set.</p> <p>Results</p> <p>In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. We demonstrate the usefulness of the method when analyzing gene sets that correspond to the KEGG pathways, and hence we called our method <it><b>P</b>athway <b>A</b>nalysis with <b>D</b>own-weighting of <b>O</b>verlapping <b>G</b>enes</it> (<b>PADOG</b>). Unlike most gene set analysis methods which are validated through the analysis of 2-3 data sets followed by a human interpretation of the results, the validation employed here uses 24 different data sets and a completely objective assessment scheme that makes minimal assumptions and eliminates the need for possibly biased human assessments of the analysis results.</p> <p>Conclusions</p> <p>PADOG significantly improves gene set ranking and boosts sensitivity of analysis using information already available in the gene expression profiles and the collection of gene sets to be analyzed. The advantages of PADOG over other existing approaches are shown to be stable to changes in the database of gene sets to be analyzed. PADOG was implemented as an R package available at: <url>http://bioinformaticsprb.med.wayne.edu/PADOG/</url>or <url>http://www.bioconductor.org</url>.</p> |
| first_indexed | 2024-12-10T12:48:03Z |
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| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-10T12:48:03Z |
| publishDate | 2012-06-01 |
| publisher | BMC |
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| spelling | doaj.art-711f577868b840f7bd22f14624067a332022-12-22T01:48:21ZengBMCBMC Bioinformatics1471-21052012-06-0113113610.1186/1471-2105-13-136Down-weighting overlapping genes improves gene set analysisTarca AdiDraghici SorinBhatti GauravRomero Roberto<p>Abstract</p> <p>Background</p> <p>The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set.</p> <p>Results</p> <p>In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. We demonstrate the usefulness of the method when analyzing gene sets that correspond to the KEGG pathways, and hence we called our method <it><b>P</b>athway <b>A</b>nalysis with <b>D</b>own-weighting of <b>O</b>verlapping <b>G</b>enes</it> (<b>PADOG</b>). Unlike most gene set analysis methods which are validated through the analysis of 2-3 data sets followed by a human interpretation of the results, the validation employed here uses 24 different data sets and a completely objective assessment scheme that makes minimal assumptions and eliminates the need for possibly biased human assessments of the analysis results.</p> <p>Conclusions</p> <p>PADOG significantly improves gene set ranking and boosts sensitivity of analysis using information already available in the gene expression profiles and the collection of gene sets to be analyzed. The advantages of PADOG over other existing approaches are shown to be stable to changes in the database of gene sets to be analyzed. PADOG was implemented as an R package available at: <url>http://bioinformaticsprb.med.wayne.edu/PADOG/</url>or <url>http://www.bioconductor.org</url>.</p>http://www.biomedcentral.com/1471-2105/13/136Gene expressionGene set analysisPathway analysisOverlapping gene sets |
| spellingShingle | Tarca Adi Draghici Sorin Bhatti Gaurav Romero Roberto Down-weighting overlapping genes improves gene set analysis BMC Bioinformatics Gene expression Gene set analysis Pathway analysis Overlapping gene sets |
| title | Down-weighting overlapping genes improves gene set analysis |
| title_full | Down-weighting overlapping genes improves gene set analysis |
| title_fullStr | Down-weighting overlapping genes improves gene set analysis |
| title_full_unstemmed | Down-weighting overlapping genes improves gene set analysis |
| title_short | Down-weighting overlapping genes improves gene set analysis |
| title_sort | down weighting overlapping genes improves gene set analysis |
| topic | Gene expression Gene set analysis Pathway analysis Overlapping gene sets |
| url | http://www.biomedcentral.com/1471-2105/13/136 |
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