Towards automatic analysis of DNA microarrays

C. Uehara; I. Kakadiaris

doi:10.1109/ACV.2002.1182157

Towards automatic analysis of DNA microarrays

C. Uehara, I. Kakadiaris

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Scopus citations

Abstract

In this paper we present a computational framework that provides the automatic analysis of spotted DNA microarray image data. The challenges are in providing an accurate representation of microarray hybridization observations while minimizing user interaction. To obtain this, we need to segment the observation data and subsequent correction for true hybridization level measurements must be accomplished against the backdrop of signal noise, background signal variation, and spatial non-uniformity in the array layout. With the requirements of automation and accuracy, an approach based on data-driven denoising, array addressing, background estimation, and spot segmentation was developed We proceeded to validate our approach on synthetic data as well as the publicly available raw and analyzed microarray data from the published Stanford yeast cell cycle analysis project. Spot mean and total intensities were examined as well as spot background estimates. By minimizing the user role, a main bottleneck in microarray data analysis is removed, allowing for more immediate analysis of large observation data sets. Our implementation has proven to be relatively fast, and the results of our approach have been encouraging.

Original language	English (US)
Title of host publication	Proceedings - 6th IEEE Workshop on Applications of Computer Vision, WACV 2002
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	57-62
Number of pages	6
ISBN (Electronic)	0769518583
DOIs	https://doi.org/10.1109/ACV.2002.1182157
State	Published - 2002
Event	6th IEEE Workshop on Applications of Computer Vision, WACV 2002 - Orlando, United States Duration: Dec 3 2002 → Dec 4 2002

Publication series

Name	Proceedings of IEEE Workshop on Applications of Computer Vision
Volume	2002-January
ISSN (Print)	2158-3978
ISSN (Electronic)	2158-3986

Conference

Conference	6th IEEE Workshop on Applications of Computer Vision, WACV 2002
Country/Territory	United States
City	Orlando
Period	12/3/02 → 12/4/02

Keywords

Automation
Background noise
DNA computing
Data analysis
Fungi
Image analysis
Image segmentation
Level measurement
Noise level
Noise reduction

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Computer Science Applications

Access to Document

10.1109/ACV.2002.1182157

Cite this

Uehara, C., & Kakadiaris, I. (2002). Towards automatic analysis of DNA microarrays. In Proceedings - 6th IEEE Workshop on Applications of Computer Vision, WACV 2002 (pp. 57-62). Article 1182157 (Proceedings of IEEE Workshop on Applications of Computer Vision; Vol. 2002-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACV.2002.1182157

Towards automatic analysis of DNA microarrays. / Uehara, C.; Kakadiaris, I.
Proceedings - 6th IEEE Workshop on Applications of Computer Vision, WACV 2002. Institute of Electrical and Electronics Engineers Inc., 2002. p. 57-62 1182157 (Proceedings of IEEE Workshop on Applications of Computer Vision; Vol. 2002-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Uehara, C & Kakadiaris, I 2002, Towards automatic analysis of DNA microarrays. in Proceedings - 6th IEEE Workshop on Applications of Computer Vision, WACV 2002., 1182157, Proceedings of IEEE Workshop on Applications of Computer Vision, vol. 2002-January, Institute of Electrical and Electronics Engineers Inc., pp. 57-62, 6th IEEE Workshop on Applications of Computer Vision, WACV 2002, Orlando, United States, 12/3/02. https://doi.org/10.1109/ACV.2002.1182157

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abstract = "In this paper we present a computational framework that provides the automatic analysis of spotted DNA microarray image data. The challenges are in providing an accurate representation of microarray hybridization observations while minimizing user interaction. To obtain this, we need to segment the observation data and subsequent correction for true hybridization level measurements must be accomplished against the backdrop of signal noise, background signal variation, and spatial non-uniformity in the array layout. With the requirements of automation and accuracy, an approach based on data-driven denoising, array addressing, background estimation, and spot segmentation was developed We proceeded to validate our approach on synthetic data as well as the publicly available raw and analyzed microarray data from the published Stanford yeast cell cycle analysis project. Spot mean and total intensities were examined as well as spot background estimates. By minimizing the user role, a main bottleneck in microarray data analysis is removed, allowing for more immediate analysis of large observation data sets. Our implementation has proven to be relatively fast, and the results of our approach have been encouraging.",

keywords = "Automation, Background noise, DNA computing, Data analysis, Fungi, Image analysis, Image segmentation, Level measurement, Noise level, Noise reduction",

author = "C. Uehara and I. Kakadiaris",

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AB - In this paper we present a computational framework that provides the automatic analysis of spotted DNA microarray image data. The challenges are in providing an accurate representation of microarray hybridization observations while minimizing user interaction. To obtain this, we need to segment the observation data and subsequent correction for true hybridization level measurements must be accomplished against the backdrop of signal noise, background signal variation, and spatial non-uniformity in the array layout. With the requirements of automation and accuracy, an approach based on data-driven denoising, array addressing, background estimation, and spot segmentation was developed We proceeded to validate our approach on synthetic data as well as the publicly available raw and analyzed microarray data from the published Stanford yeast cell cycle analysis project. Spot mean and total intensities were examined as well as spot background estimates. By minimizing the user role, a main bottleneck in microarray data analysis is removed, allowing for more immediate analysis of large observation data sets. Our implementation has proven to be relatively fast, and the results of our approach have been encouraging.

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KW - Background noise

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KW - Noise level

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Towards automatic analysis of DNA microarrays

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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