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Article: Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data

TitleFast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data
Authors
Issue Date2008
PublisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
Citation
Bioinformatics, 2008, v. 24 n. 11, p. 1349-1358 How to Cite?
AbstractMotivation: Recently developed network component analysis (NCA) approach is promising for gene regulatory network reconstruction from microarray data. The existing NCA algorithm is an iterative method which has two potential limitations: computational instability and multiple local solutions. The subsequently developed NCA-r algorithm with Tikhonov regularization can help solve the first issue but cannot completely handle the second one. Here we develop a novel Fast Network Component Analysis (FastNCA) algorithm which has an analytical solution that is much faster and does not have the above limitations. Results: Firstly FastNCA is compared to NCA and NCA-r using synthetic data. The reconstruction of FastNCA is more accurate than that of NCA-r and comparable to that of properly converged NCA. FastNCA is not sensitive to the correlation among the input signals, while its performance does degrade a little but not as dramatically as that of NCA. Like NCA, FastNCA is not very sensitive to small inaccuracies in a priori information on the network topology. FastNCA is about several tens times faster than NCA and several hundreds times faster than NCA-r. Then, the method is applied to real yeast cell-cycle microarray data. The activities of the estimated cell-cycle regulators by FastNCA and NCA-r are compared to the semi-quantitative results obtained independently by Lee et al. (2002). It is shown here that there is a greater agreement between the results of FastNCA and Lee's, which is represented by the ratio 23/33, than that between the results of NCA-r and Lee's, which is 14/33. © The Author 2008. Published by Oxford University Press. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/73958
ISSN
2015 Impact Factor: 5.766
2015 SCImago Journal Rankings: 4.643
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChang, Cen_HK
dc.contributor.authorDing, Zen_HK
dc.contributor.authorHung, YSen_HK
dc.contributor.authorFung, PCWen_HK
dc.date.accessioned2010-09-06T06:56:25Z-
dc.date.available2010-09-06T06:56:25Z-
dc.date.issued2008en_HK
dc.identifier.citationBioinformatics, 2008, v. 24 n. 11, p. 1349-1358en_HK
dc.identifier.issn1367-4803en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73958-
dc.description.abstractMotivation: Recently developed network component analysis (NCA) approach is promising for gene regulatory network reconstruction from microarray data. The existing NCA algorithm is an iterative method which has two potential limitations: computational instability and multiple local solutions. The subsequently developed NCA-r algorithm with Tikhonov regularization can help solve the first issue but cannot completely handle the second one. Here we develop a novel Fast Network Component Analysis (FastNCA) algorithm which has an analytical solution that is much faster and does not have the above limitations. Results: Firstly FastNCA is compared to NCA and NCA-r using synthetic data. The reconstruction of FastNCA is more accurate than that of NCA-r and comparable to that of properly converged NCA. FastNCA is not sensitive to the correlation among the input signals, while its performance does degrade a little but not as dramatically as that of NCA. Like NCA, FastNCA is not very sensitive to small inaccuracies in a priori information on the network topology. FastNCA is about several tens times faster than NCA and several hundreds times faster than NCA-r. Then, the method is applied to real yeast cell-cycle microarray data. The activities of the estimated cell-cycle regulators by FastNCA and NCA-r are compared to the semi-quantitative results obtained independently by Lee et al. (2002). It is shown here that there is a greater agreement between the results of FastNCA and Lee's, which is represented by the ratio 23/33, than that between the results of NCA-r and Lee's, which is 14/33. © The Author 2008. Published by Oxford University Press. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/en_HK
dc.relation.ispartofBioinformaticsen_HK
dc.rightsBioinformatics. Copyright © Oxford University Press.en_HK
dc.subject.meshAlgorithmsen_HK
dc.subject.meshComputer Simulationen_HK
dc.subject.meshGene Expression Profiling - methodsen_HK
dc.subject.meshGene Expression Regulation - physiologyen_HK
dc.subject.meshModels, Biologicalen_HK
dc.subject.meshOligonucleotide Array Sequence Analysis - methodsen_HK
dc.subject.meshPrincipal Component Analysisen_HK
dc.subject.meshProteome - metabolismen_HK
dc.subject.meshSignal Transduction - physiologyen_HK
dc.titleFast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1367-4803&volume=24&spage=1349&epage=1358&date=2008&atitle=Fast+network+component+analysis+(FastNCA)+for+gene+regulatory+network+reconstruction+from+microarray+dataen_HK
dc.identifier.emailChang, C: cqchang@eee.hku.hken_HK
dc.identifier.emailHung, YS: yshung@hkucc.hku.hken_HK
dc.identifier.authorityChang, C=rp00095en_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/bioinformatics/btn131en_HK
dc.identifier.pmid18400771-
dc.identifier.scopuseid_2-s2.0-44349190399en_HK
dc.identifier.hkuros146245en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-44349190399&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume24en_HK
dc.identifier.issue11en_HK
dc.identifier.spage1349en_HK
dc.identifier.epage1358en_HK
dc.identifier.eissn1460-2059-
dc.identifier.isiWOS:000256169300005-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChang, C=7407033052en_HK
dc.identifier.scopusauthoridDing, Z=7401550510en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK
dc.identifier.scopusauthoridFung, PCW=7101613315en_HK
dc.identifier.citeulike2682604-

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