Abstract
Differential coexpression analysis usually requires the definition of 'distance' or 'similarity' between measured datasets. Until now, the most common choice is Pearson correlation coefficient. However, Pearson correlation coefficient 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. Firstly, we calculate the biweight midcorrelation coefficients between all gene pairs. Then, we filter out non-informative correlation pairs using the 'half-thresholding' strategy and calculate the differential coexpression value of gene, The experimental results on simulated data show that the new approach performed better than three previously published differential coexpression analysis (DCEA) methods. Moreover, we use the maximum clique analysis to gene subset included genes identified by our approach and previously reported T2D-related genes, many additional discoveries can be found through our method.
Highlights
Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers [23]
We propose a new approach for gene differential coexpression analysis based on Biweight Midcorrelation and half-threshoding strategy
We evaluated BMHT method in terms of its capability to discover the differential coexpression genes from the simulated datasets, and compared it with methods, i.e., ‘Log Ratio of Connection’(LRC), ‘Average Specific Connection’(ASC), and ‘Weighted Gene Coexpression Network Analysis’(WGCNA)
Summary
DNA Microarray has been widely used as measurement tools in gene expression data analysis [1,2,3,4]. Gene expression profiling data from DNA microarray can detect the expression levels of thousands of genes simultaneously. Which provide an effective way for mining diseaserelated genes nalysis of gene expression data can be divided into three levels: firstly, analysis the expression level of individual genes, and to determine its function based on gene expression level changes under different experimental conditions. The tumor type specific genes are identified according to the significance of difference in gene expression using the statistical hypothesis testing analysis method. Attempt to deduce the potential gene regulatory networks mechanism and explain the observed gene expression data
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