Paper
4 June 2001 Statistical issues in signal extraction from microarrays
Tracy Bergemann, Filemon Quiaoit, Jeffrey J. Delrow, Lue Ping Zhao
Author Affiliations +
Abstract
Microarray technologies are increasingly used in biomedical research to study genome-wide expression profiles in the post genomic era. Their popularity is largely due to their high throughput and economical affordability. For example, microarrays have been applied to studies of cell cycle, regulatory circuitry, cancer cell lines, tumor tissues, and drug discoveries. One obstacle facing the continued success of applying microarray technologies, however, is the random variaton present on microarrays: within signal spots, between spots and among chips. In addition, signals extracted by available software packages seem to vary significantly. Despite a variety of software packages, it appears that there are two major approaches to signal extraction. One approach is to focus on the identification of signal regions and hence estimation of signal levels above background levels. The other approach is to use the distribution of intensity values as a way of identifying relevant signals. Building upon both approaches, the objective of our work is to develop a method that is statistically rigorous and also efficient and robust. Statistical issues to be considered here include: (1) how to refine grid alignment so that the overall variation is minimized, (2) how to estimate the signal levels relative to the local background levels as well as the variance of this estimate, and (3) how to integrate red and green channel signals so that the ratio of interest is stable, simultaneously relaxing distributional assumptions.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tracy Bergemann, Filemon Quiaoit, Jeffrey J. Delrow, and Lue Ping Zhao "Statistical issues in signal extraction from microarrays", Proc. SPIE 4266, Microarrays: Optical Technologies and Informatics, (4 June 2001); https://doi.org/10.1117/12.427997
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Cited by 4 scholarly publications.
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KEYWORDS
Image segmentation

Statistical analysis

Cancer

Image analysis

Nonlinear filtering

Rubidium

Analytical research

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