Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images
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Gierahn-2014-Crossword-A Fully.pdf
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Author(s)
Gierahn, Todd Michael
Loginov, Denis
Love, J. Christopher
Love, John C
Date Issued
February 2014
Journal
Journal of Proteome Research
Publisher
American Chemical Society (ACS)
Citation
Gierahn, Todd M., Denis Loginov, and J. Christopher Love. “Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images.” Journal of Proteome Research 13, no. 2 (February 7, 2014): 362–371. © 2014 American Chemical Society.
Version
Final published version
Abstract
Biological assays formatted as microarrays have become a critical tool for the generation of the comprehensive data sets required for systems-level understanding of biological processes. Manual annotation of data extracted from images of microarrays, however, remains a significant bottleneck, particularly for protein microarrays due to the sensitivity of this technology to weak artifact signal. In order to automate the extraction and curation of data from protein microarrays, we describe an algorithm called Crossword that logically combines information from multiple approaches to fully automate microarray segmentation. Automated artifact removal is also accomplished by segregating structured pixels from the background noise using iterative clustering and pixel connectivity. Correlation of the location of structured pixels across image channels is used to identify and remove artifact pixels from the image prior to data extraction. This component improves the accuracy of data sets while reducing the requirement for time-consuming visual inspection of the data. Crossword enables a fully automated protocol that is robust to significant spatial and intensity aberrations. Overall, the average amount of user intervention is reduced by an order of magnitude and the data quality is increased through artifact removal and reduced user variability. The increase in throughput should aid the further implementation of microarray technologies in clinical studies.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Ragon Institute of MGH, MIT and Harvard
Koch Institute for Integrative Cancer Research at MIT
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1021/pr401167h
