Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/19224
Title: Dealing with missing values in DNA microarray
Authors: CAO YI
Keywords: microarray data mining, missing value imputation, non-parametric regression, principal component analysis, robust regression, clustering analysis
Issue Date: 19-Aug-2008
Source: CAO YI (2008-08-19). Dealing with missing values in DNA microarray. ScholarBank@NUS Repository.
Abstract: Microarray data has been used in a large number of studies covering a broad range of areas in biology. Missing values are often encountered when analyzing microarray gene expression data. It is essential that the estimates for the missing gene expression values are accurate to make the subsequent analysis as informative as possible.In this study, we first develop nonparametric regression approach (NPRA) for imputation, which can capture both linear and non-linear relations between genes. We further take advantage of relations between arrays to improve imputation accuracy. In order to deal with outliers in microarray, we employ robust principal component analysis (RPCA) imputation method.Furthermore, we construct a missing value imputation framework, by combining the estimates from NPRA and RPCA respectively. Finally, we focus on investigating the impact of missing values and imputation on gene clustering analysis, and justify that clustering accuracy is also a measure to assess imputation methods.
URI: http://scholarbank.nus.edu.sg/handle/10635/19224
Appears in Collections:Ph.D Theses (Open)

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