Title | WaveRead: automatic measurement of relative gene expression levels from microarrays using wavelet analysis. |
Publication Type | Journal Article |
Year of Publication | 2006 |
Authors | Bidaut, G, Manion, FJ, Garcia, C, Ochs, MF |
Journal | J Biomed Inform |
Volume | 39 |
Issue | 4 |
Pagination | 379-88 |
Date Published | 2006 Aug |
ISSN | 1532-0480 |
Keywords | Algorithms, Artificial Intelligence, Gene Expression, Gene Expression Profiling, Oligonucleotide Array Sequence Analysis, Pattern Recognition, Automated, Software |
Abstract | Gene expression microarrays monitor the expression levels of thousands of genes in an experiment simultaneously. To utilize the information generated, each of the thousands of spots on a microarray image must be properly quantified, including background correction. Most present methods require manual alignment of grids to the image data, and still often require additional minor adjustments on a spot by spot basis to correct for spotting irregularities. Such intervention is time consuming and also introduces inconsistency in the handling of data. A fully automatic, tested system would increase throughput and reliability in this field. In this paper, we describe WaveRead, a fully automated, standalone, open-source system for quantifying gene expression array images. Through the use of wavelet analysis to identify the spot locations and diameters, the system is able to automatically grid the image and quantify signal intensities and background corrections without any user intervention. The ability of WaveRead to perform proper quantification is demonstrated by analysis of both simulated images containing spots with donut shapes, elliptical shapes, and Gaussian intensity distributions, as well as of standard images from the National Cancer Institute. |
DOI | 10.1016/j.jbi.2005.10.001 |
Alternate Journal | J Biomed Inform |
PubMed ID | 16298556 |
Grant List | CA06927 / CA / NCI NIH HHS / United States P50 CA83638 / CA / NCI NIH HHS / United States |
Research reported in this publication was supported by the National Cancer Institutes of
Health under Award Number P30CA046592. The content is solely the responsibility
of the authors and does not necessarily represent the official views of the
National Institutes of Health.
Research reported in this publication was supported by the National Cancer Institutes of
Health under Award Number P30CA046592 by the use of the following Cancer Center
Shared Resource(s): Biostatistics, Analytics & Bioinformatics; Flow Cytometry;
Transgenic Animal Models; Tissue and Molecular Pathology; Structure & Drug
Screening; Cell & Tissue Imaging; Experimental Irradiation; Preclinical
Imaging & Computational Analysis; Health Communications; Immune Monitoring;
Pharmacokinetics)
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