Biweight Midcorrelation-Based Gene Differential Coexpression Analysis and Its Application to Type II Diabetes

Differential coexpression analysis usually requires the definition of ‘distance’ or ‘similarity’ between measured datasets, the most common choices being Pearson correlation. However, Pearson correlation is sensitive to outliers. Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers. In this paper, we introduce to use Biweight Midcorrelation to measure ‘similarity’ between gene expression profiles, and provide a new approach for gene differential coexpression analysis. The results show that the new approach performed better than three previously published differential coexpression analysis (DCEA) methods. We applied the new approach to a public available type 2 diabetes (T2D) expression dataset, and many additional discoveries can be found through our method.

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