Mining Gene Expression Profile with Missing Values: An Integration of Kernel PCA and Robust Singular Values Decomposition

Abstract

Background: Gene expression profiling and transcriptomics provide valuable information about the role of genes that are differentially expressed between two or more samples. It is always important and challenging to analyse High-throughput DNA microarray data with a number of missing values under various experimental conditions. </P><P> Objectives: Graphical data visualizations of the expression of all genes in a particular cell provide holistic views of gene expression patterns, which improve our understanding of cellular systems under normal and pathological conditions. However, current visualization methods are sensitive to missing values, which are frequently observed in microarray-based gene expression profiling, potentially affecting the subsequent statistical analyses. Methods: We addressed in this study the problem of missing values with respect to different imputation methods using gene expression biplot (GE biplot), one of the most popular gene visualization techniques. The effects of missing values for mining differentially expressed genes in gene expression data were evaluated using four well-known imputation methods: Robust Singular Value Decomposition (Robust SVD), Column Average (CA), Column Median (CM), and K-nearest Neighbors (KNN). Frobenius norm and absolute distances were used to measure the accuracy of the methods. Results: Three numerical experiments were performed using simulated data (i) and publicly available colon cancer (ii) and leukemia data (iii) to analyze the performance of each method. The results showed that CM and KNN performed better than Robust SVD and CA for identifying the index gene profile in the biplot visualization in both the simulation study and the colon cancer and leukemia microarray datasets. Conclusion: The impact of missing values on the GE biplot was smaller when the data matrix was imputed by KNN than by CM. This study concluded that KNN performed satisfactorily in generating a GE biplot in the presence of missing values in microarray data.

Missing Value Estimation Methods Research for Arrhythmia Classification Using the Modified Kernel Difference-Weighted KNN Algorithms

“…For missing values, some studies apply the “case deletion” method directly, i.e., simply removing those instances with missing values and only using the observed instances to establish the classification models, which may lose some information especially for small sample datasets [ 11 ]. To tackle these shortcomings, in the past decades, several missing value imputation methods have been proposed in some fields like DNA microarrays [ 12 – 16 ] and traffic data problems [ 17 , 18 ]. For example, Troyanskaya et al present a prevalent imputation method based on KNN, i.e., KNN impute for DNA microarrays [ 13 ].…”