Microarray Missing Data Imputation Using Regression

Tuncay Bayrak and Hasan Ogul


Gene expression prediction, Missing value imputation, Regression, Relevance vector machine


Having missing values due to several experimental conditions is a common problem in analyzing the results of microarray experiments. Although many imputation methods exist, comparative studies based on regression based models are very limited. Particularly, Relevance Vector Machine (RVM), a recent regression method shown to be effective in various domains, has not been considered so far for missing value imputation in microarray data. In this study, we present a comparative study between regression based models, including linear regression, k-nearest neighbor regression and RVM that uses data obtained from breast, colon and prostate cancer tissues through the microarray technology. The leave-one-out (or Jackknife) procedure is applied for the validation. To measure the performance of the model we used Spearman correlation coefficient (CC). The results reveal that RVM with a Gaussian kernel outperforms other regression models in some cases.

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