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Microarray enriched gene rank
BACKGROUND: We develop a new concept that reflects how genes are connected based on microarray data using the coefficient of determination (the squared Pearson correlation coefficient). Our gene rank combines a priori knowledge about gene connectivity, say, from the Gene Ontology (GO) database, and...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4305247/ https://www.ncbi.nlm.nih.gov/pubmed/25649242 http://dx.doi.org/10.1186/s13040-014-0033-1 |
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author | Demidenko, Eugene |
author_facet | Demidenko, Eugene |
author_sort | Demidenko, Eugene |
collection | PubMed |
description | BACKGROUND: We develop a new concept that reflects how genes are connected based on microarray data using the coefficient of determination (the squared Pearson correlation coefficient). Our gene rank combines a priori knowledge about gene connectivity, say, from the Gene Ontology (GO) database, and the microarray expression data at hand, called the microarray enriched gene rank, or simply gene rank (GR). GR, similarly to Google PageRank, is defined in a recursive fashion and is computed as the left maximum eigenvector of a stochastic matrix derived from microarray expression data. An efficient algorithm is devised that allows computation of GR for 50 thousand genes with 500 samples within minutes on a personal computer using the public domain statistical package R. RESULTS: Computation of GR is illustrated with several microarray data sets. In particular, we apply GR (1) to answer whether bad genes are more connected than good genes in relation with cancer patient survival, (2) to associate gene connectivity with cluster/subtypes in ovarian cancer tumors, and to determine whether gene connectivity changes (3) from organ to organ within the same organism and (4) between organisms. CONCLUSIONS: We have shown by examples that findings based on GR confirm biological expectations. GR may be used for hypothesis generation on gene pathways. It may be used for a homogeneous sample or for comparison of gene connectivity among cases and controls, or in longitudinal setting. |
format | Online Article Text |
id | pubmed-4305247 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-43052472015-02-03 Microarray enriched gene rank Demidenko, Eugene BioData Min Research BACKGROUND: We develop a new concept that reflects how genes are connected based on microarray data using the coefficient of determination (the squared Pearson correlation coefficient). Our gene rank combines a priori knowledge about gene connectivity, say, from the Gene Ontology (GO) database, and the microarray expression data at hand, called the microarray enriched gene rank, or simply gene rank (GR). GR, similarly to Google PageRank, is defined in a recursive fashion and is computed as the left maximum eigenvector of a stochastic matrix derived from microarray expression data. An efficient algorithm is devised that allows computation of GR for 50 thousand genes with 500 samples within minutes on a personal computer using the public domain statistical package R. RESULTS: Computation of GR is illustrated with several microarray data sets. In particular, we apply GR (1) to answer whether bad genes are more connected than good genes in relation with cancer patient survival, (2) to associate gene connectivity with cluster/subtypes in ovarian cancer tumors, and to determine whether gene connectivity changes (3) from organ to organ within the same organism and (4) between organisms. CONCLUSIONS: We have shown by examples that findings based on GR confirm biological expectations. GR may be used for hypothesis generation on gene pathways. It may be used for a homogeneous sample or for comparison of gene connectivity among cases and controls, or in longitudinal setting. BioMed Central 2015-01-17 /pmc/articles/PMC4305247/ /pubmed/25649242 http://dx.doi.org/10.1186/s13040-014-0033-1 Text en © Demidenko; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Demidenko, Eugene Microarray enriched gene rank |
title | Microarray enriched gene rank |
title_full | Microarray enriched gene rank |
title_fullStr | Microarray enriched gene rank |
title_full_unstemmed | Microarray enriched gene rank |
title_short | Microarray enriched gene rank |
title_sort | microarray enriched gene rank |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4305247/ https://www.ncbi.nlm.nih.gov/pubmed/25649242 http://dx.doi.org/10.1186/s13040-014-0033-1 |
work_keys_str_mv | AT demidenkoeugene microarrayenrichedgenerank |