Nonlinear dimensionality reduction of gene expression data for visualization and clustering analysis of cancer tissue samples

Computers in Biology and Medicine
Jinlong Shi, Zhigang Luo

Abstract

Gene expression data are the representation of nonlinear interactions among genes and environmental factors. Computing analysis of these data is expected to gain knowledge of gene functions and disease mechanisms. Clustering is a classical exploratory technique of discovering similar expression patterns and function modules. However, gene expression data are usually of high dimensions and relatively small samples, which results in the main difficulty for the application of clustering algorithms. Principal component analysis (PCA) is usually used to reduce the data dimensions for further clustering analysis. While PCA estimates the similarity between expression profiles based on the Euclidean distance, which cannot reveal the nonlinear connections between genes. This paper uses nonlinear dimensionality reduction (NDR) as a preprocessing strategy for feature selection and visualization, and then applies clustering algorithms to the reduced feature spaces. In order to estimate the effectiveness of NDR for capturing biologically relevant structures, the comparative analysis between NDR and PCA is exploited to five real cancer expression datasets. Results show that NDR can perform better than PCA in visualization and clustering anal...Continue Reading

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Citations

Nov 21, 2012·Biomedical Engineering Online·Steven C DingerDavid M Rubin
Jan 3, 2018·Nucleic Acids Research·Gary Wilk, Rosemary Braun
Jul 22, 2011·American Journal of Physiology. Regulatory, Integrative and Comparative Physiology·Reinald Pamplona, David Costantini
Jan 2, 2020·Journal of Biomedical Informatics·Omar Rafique, A H Mir

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