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dc.contributor.authorLiew, Alan Wee-Chung
dc.contributor.authorXian, Jun
dc.contributor.authorWu, Shuanhu
dc.contributor.authorSmith, David
dc.contributor.authorYan, Hong
dc.date.accessioned2017-05-03T15:20:32Z
dc.date.available2017-05-03T15:20:32Z
dc.date.issued2007
dc.date.modified2009-10-16T05:19:57Z
dc.identifier.issn1471-2105
dc.identifier.doi10.1186/1471-2105-8-137
dc.identifier.urihttp://hdl.handle.net/10072/17077
dc.description.abstractBackground: Periodogram analysis of time-series is widespread in biology. A new challenge for analyzing the microarray time series data is to identify genes that are periodically expressed. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, and unevenly sampled time points. Most methods used in the literature operate on evenly sampled time series and are not suitable for unevenly sampled time series. Results: For evenly sampled data, methods based on the classical Fourier periodogram are often used to detect periodically expressed gene. Recently, the Lomb-Scargle algorithm has been applied to unevenly sampled gene expression data for spectral estimation. However, since the Lomb-Scargle method assumes that there is a single stationary sinusoid wave with infinite support, it introduces spurious periodic components in the periodogram for data with a finite length. In this paper, we propose a new spectral estimation algorithm for unevenly sampled gene expression data. The new method is based on signal reconstruction in a shift-invariant signal space, where a direct spectral estimation procedure is developed using the B-spline basis. Experiments on simulated noisy gene expression profiles show that our algorithm is superior to the Lomb-Scargle algorithm and the classical Fourier periodogram based method in detecting periodically expressed genes. We have applied our algorithm to the Plasmodium falciparum and Yeast gene expression data and the results show that the algorithm is able to detect biologically meaningful periodically expressed genes. Conclusion: We have proposed an effective method for identifying periodic genes in unevenly sampled space of microarray time series gene expression data. The method can also be used as an effective tool for gene expression time series interpolation or resampling.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent53118 bytes
dc.format.extent1384410 bytes
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherBioMed Central
dc.publisher.placeUnited Kingdom
dc.publisher.urihttp://www.biomedcentral.com/
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto19
dc.relation.ispartofjournalBMC Bioinformatics
dc.relation.ispartofvolume8
dc.rights.retentionN
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode31
dc.subject.fieldofresearchcode46
dc.titleSpectral estimation in unevenly sampled space of periodically expressed microarray time series data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/2.0
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.description.notepublicPage numbers are not for citation purposes. Instead, this article has the unique article number of 137.
gro.rights.copyright© 2007 Liew et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.date.issued2007
gro.hasfulltextFull Text
gro.griffith.authorLiew, Alan Wee-Chung


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