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Identifying Coexpressed Genes

In: Statistical Methods for Biostatistics and Related Fields

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  • Qihua Wang

Abstract

Some gene expression data contain outliers and noise because of experiment error. In clustering, outliers and noise can result in false positives and false negatives. This motivates us to develop a weighting method to adjust the expression data such that the outlier and noise effect decrease, and hence result in a reduction in false positives and false negatives in clustering. In this paper, we describe the weighting adjustment method and apply it to a yeast cell cycle data set. Based on the adjusted yeast cell cycle expression data, the hierarchical clustering method with a correlation coefficient measure performs better than that based on standardized expression data. The clustering method based on the adjusted data can group some functionally related genes together and yields higher quality clusters.

Suggested Citation

  • Qihua Wang, 2007. "Identifying Coexpressed Genes," Springer Books, in: Statistical Methods for Biostatistics and Related Fields, chapter 7, pages 125-145, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-32691-5_7
    DOI: 10.1007/978-3-540-32691-5_7
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