Shrinkage covariance matrix approach based on robust trimmed mean in gene sets detection

Abstract

Microarray involves of placing an orderly arrangement of thousands of gene sequences in a grid on a suitable surface. The technology has made a novelty discovery since its development and obtained an increasing attention among researchers. The widespread of microarray technology is largely due to its ability to perform simultaneous analysis of thousands of genes in a massively parallel manner in one experiment. Hence, it provides valuable knowledge on gene interaction and function. The microarray data set typically consists of tens of thousands of genes (variables) from just dozens of samples due to various constraints. Therefore, the sample covariance matrix in Hotelling's T2 statistic is not positive definite and become singular, thus it cannot be inverted. In this research, the Hotelling's T2 statistic is combined with a shrinkage approach as an alternative estimation to estimate the covariance matrix to detect significant gene sets. The use of shrinkage covariance matrix overcomes the singularity problem by converting an unbiased to an improved biased estimator of covariance matrix. Robust trimmed mean is integrated into the shrinkage matrix to reduce the influence of outliers and consequently increases its efficiency. The performance of the proposed method is measured using several simulation designs. The results are expected to outperform existing techniques in many tested conditions.Microarray involves of placing an orderly arrangement of thousands of gene sequences in a grid on a suitable surface. The technology has made a novelty discovery since its development and obtained an increasing attention among researchers. The widespread of microarray technology is largely due to its ability to perform simultaneous analysis of thousands of genes in a massively parallel manner in one experiment. Hence, it provides valuable knowledge on gene interaction and function. The microarray data set typically consists of tens of thousands of genes (variables) from just dozens of samples due to various constraints. Therefore, the sample covariance matrix in Hotelling's T2 statistic is not positive definite and become singular, thus it cannot be inverted. In this research, the Hotelling's T2 statistic is combined with a shrinkage approach as an alternative estimation to estimate the covariance matrix to detect significant gene sets. The use of shrinkage covariance matrix overcomes the singularity prob...