IMPROVED ESTIMATION OF COVARIANCE MATRIX IN HOTELLING'S T2 FOR MICROARRAY DATA
DOI:
https://doi.org/10.22159/ijpps.2016v8s2.15215Keywords:
Gene set analysis, Hotelling`s T2, Microarray analysis, Shrinkage covariance matrixAbstract
The relationship between genes in gene set analysis in microarray data is analyzed using Hotelling's T2 but the test cannot be applied when the number of samples is larger than the number of variables which is uncommon in the microarray. Thus, in this study, we proposed shrinkage approaches to estimating the covariance matrix in Hotelling's T2 particularly to cater high dimensionality problem in microarray data. Three shrinkage covariance methods were proposed in this study and are referred as Shrink A, Shrink B and Shrink C. The analysis of the three proposed shrinkage methods was compared with the Regularized Covariance Matrix Approach and Kong's Principal Component Analysis. The performances of the proposed methods were assessed using several cases of simulated data sets. In many cases, the Shrink A method performed the best, followed by the Shrink C and RCMAT methods. In contrast, both the Shrink B and KPCA methods showed relatively poor results. The study contributes to an establishment of modified multivariate approach to differential gene expression analysis and expected to be applied in other areas with similar data characteristics.
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