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Proceedings Article

Multivariate curve resolution for hyperspectral image analysis: applications to microarray technology

[+] Author Affiliations
David M. Haaland, Jerilyn A. Timlin, Michael B. Sinclair, Mark H. Van Benthem

Sandia National Labs. (USA)

M. Juanita Martinez, Anthony D. Aragon, Margaret Werner-Washburne

Univ. of New Mexico (USA)

Proc. SPIE 4959, Spectral Imaging: Instrumentation, Applications, and Analysis II, 55 (July 1, 2003); doi:10.1117/12.477945
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From Conference Volume 4959

  • Spectral Imaging: Instrumentation, Applications, and Analysis II
  • Richard M. Levenson; Gregory H. Bearman; Anita Mahadevan-Jansen
  • San Jose, CA | January 25, 2003

abstract

Multivariate curve resolution (MCR) using constrained alternating least squares algorithms represents a powerful analysis capability for a quantitative analysis of hyperspectral image data. We will demonstrate the application of MCR using data from a new hyperspectral fluorescence imaging microarray scanner for monitoring gene expression in cells from thousands of genes on the array. The new scanner collects the entire fluorescence spectrum from each pixel of the scanned microarray. Application of MCR with nonnegativity and equality constraints reveals several sources of undesired fluorescence that emit in the same wavelength range as the reporter fluorphores. MCR analysis of the hyperspectral images confirms that one of the sources of fluorescence is due to contaminant fluorescence under the printed DNA spots that is spot localized. Thus, traditional background subtraction methods used with data collected from the current commercial microarray scanners will lead to errors in determining the relative expression of low-expressed genes. With the new scanner and MCR analysis, we generate relative concentration maps of the background, impurity, and fluroescent labels over the entire image. Since the concentration maps of the fluorescent labels are relativly uaffected by the presence of background and impurity emissions, the accuracy and useful dynamic range of the gene expression data are both greatly improved over those obtained by commercial microarray scanners.

© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

David M. Haaland ; Jerilyn A. Timlin ; Michael B. Sinclair ; Mark H. Van Benthem ; M. Juanita Martinez, et al.
"Multivariate curve resolution for hyperspectral image analysis: applications to microarray technology", Proc. SPIE 4959, Spectral Imaging: Instrumentation, Applications, and Analysis II, 55 (July 1, 2003); doi:10.1117/12.477945; http://dx.doi.org/10.1117/12.477945


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