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Correlation Statistics for cDNA Microarray Image Analysis
July-September 2006 (vol. 3 no. 3)
pp. 232-238
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.

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Index Terms:
Microarrays, image segmentation, Morgera's covariance complexity, Pearson's correlation, Spearman's rank correlation.
Radhakrishnan Nagarajan, Meenakshi Upreti, "Correlation Statistics for cDNA Microarray Image Analysis," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 3, no. 3, pp. 232-238, July-Sept. 2006, doi:10.1109/TCBB.2006.30
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