Gene Vector Analysis (Geneva): A unified method to detect differentially-regulated gene sets and similar microarray experiments
<p>Abstract</p> <p>Background</p> <p>Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fishe...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2008-08-01
|
| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/9/348 |
| _version_ | 1811293290731929600 |
|---|---|
| author | Agarwal Pankaj Tanner Stephen W |
| author_facet | Agarwal Pankaj Tanner Stephen W |
| author_sort | Agarwal Pankaj |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fisher Exact Test (as used in multiple GO enrichment tools), Parametric Analysis of Gene Expression (PAGE), Gene Set Enrichment Analysis (GSEA), and the connectivity map.</p> <p>Results</p> <p>We describe an analytical method (Geneva: Gene Vector Analysis) to relate genes to biological properties and to other similar experiments in a uniform way. This new method works on both gene sets and on gene lists/vectors as input queries, and can effectively query databases consisting of sets of biologically related sets, or of results from other microarray experiments. We also present an improvement to the null model estimate by using the empirical background distribution drawn from previous experiments. We validated Geneva by rediscovering a number of previous findings, and by finding significant relationships within microarrays in the GEO repository.</p> <p>Conclusion</p> <p>Provided a reasonable corpus of previous experiments is available, this method is more accurate than the class label permutation model, especially for data sets with limited number of replicates. Geneva is, moreover, computationally faster because the background distributions can be precomputed. We also provide a standard evaluation data set based on 5 pairs of related experiments that should share similar functional relationships and 28 pairs of unrelated experiments from GEO. Discovering relationships amongst GEO data sets has implications for drug repositioning, and understanding relationships between diseases and drugs.</p> |
| first_indexed | 2024-04-13T04:59:14Z |
| format | Article |
| id | doaj.art-bde31d25d86b463691dfe275029a652f |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T04:59:14Z |
| publishDate | 2008-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-bde31d25d86b463691dfe275029a652f2022-12-22T03:01:23ZengBMCBMC Bioinformatics1471-21052008-08-019134810.1186/1471-2105-9-348Gene Vector Analysis (Geneva): A unified method to detect differentially-regulated gene sets and similar microarray experimentsAgarwal PankajTanner Stephen W<p>Abstract</p> <p>Background</p> <p>Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fisher Exact Test (as used in multiple GO enrichment tools), Parametric Analysis of Gene Expression (PAGE), Gene Set Enrichment Analysis (GSEA), and the connectivity map.</p> <p>Results</p> <p>We describe an analytical method (Geneva: Gene Vector Analysis) to relate genes to biological properties and to other similar experiments in a uniform way. This new method works on both gene sets and on gene lists/vectors as input queries, and can effectively query databases consisting of sets of biologically related sets, or of results from other microarray experiments. We also present an improvement to the null model estimate by using the empirical background distribution drawn from previous experiments. We validated Geneva by rediscovering a number of previous findings, and by finding significant relationships within microarrays in the GEO repository.</p> <p>Conclusion</p> <p>Provided a reasonable corpus of previous experiments is available, this method is more accurate than the class label permutation model, especially for data sets with limited number of replicates. Geneva is, moreover, computationally faster because the background distributions can be precomputed. We also provide a standard evaluation data set based on 5 pairs of related experiments that should share similar functional relationships and 28 pairs of unrelated experiments from GEO. Discovering relationships amongst GEO data sets has implications for drug repositioning, and understanding relationships between diseases and drugs.</p>http://www.biomedcentral.com/1471-2105/9/348 |
| spellingShingle | Agarwal Pankaj Tanner Stephen W Gene Vector Analysis (Geneva): A unified method to detect differentially-regulated gene sets and similar microarray experiments BMC Bioinformatics |
| title | Gene Vector Analysis (Geneva): A unified method to detect differentially-regulated gene sets and similar microarray experiments |
| title_full | Gene Vector Analysis (Geneva): A unified method to detect differentially-regulated gene sets and similar microarray experiments |
| title_fullStr | Gene Vector Analysis (Geneva): A unified method to detect differentially-regulated gene sets and similar microarray experiments |
| title_full_unstemmed | Gene Vector Analysis (Geneva): A unified method to detect differentially-regulated gene sets and similar microarray experiments |
| title_short | Gene Vector Analysis (Geneva): A unified method to detect differentially-regulated gene sets and similar microarray experiments |
| title_sort | gene vector analysis geneva a unified method to detect differentially regulated gene sets and similar microarray experiments |
| url | http://www.biomedcentral.com/1471-2105/9/348 |
| work_keys_str_mv | AT agarwalpankaj genevectoranalysisgenevaaunifiedmethodtodetectdifferentiallyregulatedgenesetsandsimilarmicroarrayexperiments AT tannerstephenw genevectoranalysisgenevaaunifiedmethodtodetectdifferentiallyregulatedgenesetsandsimilarmicroarrayexperiments |