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In this paper, correlation of the pixels comprising a microarray spot is investigated. Subsequently, correlation statistics, namely, Pearson correlation and Spearman rank correlation, are used to segment the foreground and background intensity of microarray spots. The performance of correlation-based segmentation is compared to clustering-based (PAM, k-means) and seeded-region growing techniques (SPOT). It is shown that correlation-based segmentation is useful in flagging poorly hybridized spots, thus minimizing false-positives. The present study also raises the intriguing question of whether a change in correlation can be an indicator of differential gene expression
Published in:
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
(Volume:3
,
Issue:
3
)
Date of Publication: July-Sept. 2006
- Page(s):
- 232 - 238
- ISSN :
- 1545-5963
- INSPEC Accession Number:
- 9067094
- Digital Object Identifier :
- 10.1109/TCBB.2006.30
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 07 August 2006
- Issue Date :
- July-Sept. 2006
- Sponsored by :
- IEEE Computational Intelligence Society
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