Improving gene quantification by adjustable spot-image restoration

dc.contributor.author	Δασκαλάκης, Αντώνης	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Μπουγιούκος, Παναγιώτης	el
dc.contributor.author	Κωστόπουλος, Σπυρίδων	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.date.accessioned	2015-05-12T13:58:43Z
dc.date.available	2015-05-12T13:58:43Z
dc.date.issued	2015-05-12
dc.identifier.uri	http://hdl.handle.net/11400/10205
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://bioinformatics.oxfordjournals.org/content/23/17/2265.short	en
dc.subject	Image analysis
dc.subject	Microarray images
dc.subject	Εικόνες μικροσυστοιχιών
dc.subject	Ανάλυση εικόνας
dc.title	Improving gene quantification by adjustable spot-image restoration	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Biomedical engineering
heal.classification	Τεχνολογία
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh98002813
heal.contributorName	Καλατζής, Ιωάννης	el
heal.contributorName	Καγκάδης, Γεώργιος Χ.	el
heal.contributorName	Αργυρόπουλος, Χρήστος	el
heal.contributorName	Νικηφορίδης, Γεώργιος Σ.	el
heal.identifier.secondary	doi: 10.1093/bioinformatics/btm337
heal.language	en
heal.access	free
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2007
heal.bibliographicCitation	Daskalakis, A., Cavouras, D., Bougioukos, P., Kostopoulos, S., Glotsos, D., et al. (2007). Improving gene quantification by adjustable spot-image restoration. Bioinformatics. 23(17). pp. 2265-2272. Oxford University Press: 2007. Available from: http://bioinformatics.oxfordjournals.org/content/23/17/2265.short [Accessed 28/06/2007]	en
heal.abstract	Motivation: One of the major factors that complicate the task of microarray image analysis is that microarray images are distorted by various types of noise. In this study a robust framework is proposed, designed to take into account the effect of noise in microarray images in order to assist the demanding task of microarray image analysis. The proposed framework, incorporates in the microarray image processing pipeline a novel combination of spot adjustable image analysis and processing techniques and consists of the following stages: (1) gridding for facilitating spot identification, (2) clustering (unsupervised discrimination between spot and background pixels) applied to spot image for automatic local noise assessment, (3) modeling of local image restoration process for spot image conditioning (adjustable wiener restoration using an empirically determined degradation function), (4) automatic spot segmentation employing seeded-region-growing, (5) intensity extraction and (6) assessment of the reproducibility (real data) and the validity (simulated data) of the extracted gene expression levels. Results: Both simulated and real microarray images were employed in order to assess the performance of the proposed framework against well-established methods implemented in publicly available software packages (Scanalyze and SPOT). Regarding simulated images, the novel combination of techniques, introduced in the proposed framework, rendered the detection of spot areas and the extraction of spot intensities more accurate. Furthermore, on real images the proposed framework proved of better stability across replicates. Results indicate that the proposed framework improves spots’ segmentation and, consequently, quantification of gene expression levels. Availability: All algorithms were implemented in Matlab™ (The Mathworks, Inc., Natick, MA, USA) environment. The codes that implement microarray gridding, adaptive spot restoration and segmentation/intensity extraction are available upon request. Supplementary results and the simulated microarray images used in this study are available for download from: ftp://users:bioinformatics@mipa.med.upatras.gr	en
heal.publisher	Oxford University Press	en
heal.journalName	Bioinformatics	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true