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Correlation statistics for cDNA microarray image analysis
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DOI：10.1109/TCBB.2006.30.png)
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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.
Keyword:
microarrays
image segmentation
Morgera's covariance complexity
Pearson's correlation
Spearman's rank correlation
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Microarray expression profiling identifies genes with altered expression in HDL-deficient mice.png)
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