A novel approach to missing data estimation technique for microarray gene expression data and dimensionality reduction

Abstract

Microarray gene expression data analysis is one of the finest areas of gene expression analysis, where each gene with its expression value is useful to decide the future analysis of different genes and its characteristics values. Usually, when a data undergoes analysis consisting of missing values and the analysis performed on this data may lead to inconsistent results. We need to recover all these missing values before performing the data analysis, which incurs in the data set. This paper brings out a new method of missing data estimation with the help of clustering technique like DBSCAN for estimating missing values. We also found similar characteristic gene clustering and applied separately to the missing data estimation on these clusters. So, it is a two-step process of missing data estimation, and has an advantage in the context of data reduction dimensionally and smooths application of missing data estimation algorithm. By conducting, an experiment on two microarray data sets, its result, and performance analysis are recorded.