Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images
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, partic...
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| Format: | Article |
| Language: | en_US |
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American Chemical Society (ACS)
2015
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| Online Access: | http://hdl.handle.net/1721.1/96765 https://orcid.org/0000-0003-0921-3144 |
| _version_ | 1826190878623924224 |
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| author | Gierahn, Todd Michael Loginov, Denis Love, J. Christopher Love, John C |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering Gierahn, Todd Michael Loginov, Denis Love, J. Christopher Love, John C |
| author_sort | Gierahn, Todd Michael |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:47:01Z |
| format | Article |
| id | mit-1721.1/96765 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:47:01Z |
| publishDate | 2015 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/967652022-09-23T14:29:31Z Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images Gierahn, Todd Michael Loginov, Denis Love, J. Christopher Love, John C 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 Gierahn, Todd Michael Loginov, Denis Love, J. Christopher 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. Camille and Henry Dreyfus Foundation (Camille Dreyfus Teacher-Scholar Award) 2015-04-23T19:41:53Z 2015-04-23T19:41:53Z 2014-02 2013-03 Article http://purl.org/eprint/type/JournalArticle 1535-3893 1535-3907 http://hdl.handle.net/1721.1/96765 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. https://orcid.org/0000-0003-0921-3144 en_US http://dx.doi.org/10.1021/pr401167h Journal of Proteome Research 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. application/pdf American Chemical Society (ACS) American Chemical Society |
| spellingShingle | Gierahn, Todd Michael Loginov, Denis Love, J. Christopher Love, John C Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images |
| title | Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images |
| title_full | Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images |
| title_fullStr | Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images |
| title_full_unstemmed | Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images |
| title_short | Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images |
| title_sort | crossword a fully automated algorithm for the segmentation and quality control of protein microarray images |
| url | http://hdl.handle.net/1721.1/96765 https://orcid.org/0000-0003-0921-3144 |
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