A Robust Manifold Graph Regularized Nonnegative Matrix Factorization Algorithm for Cancer Gene Clustering.

Rong Zhu,Ying Guo,Yuan-Ke Zhang,Jin-Xing Liu

doi:10.3390/molecules22122131

Abstract

Detecting genomes with similar expression patterns using clustering techniques plays an important role in gene expression data analysis. Non-negative matrix factorization (NMF) is an effective method for clustering the analysis of gene expression data. However, the NMF-based method is performed within the Euclidean space, and it is usually inappropriate for revealing the intrinsic geometric structure of data space. In order to overcome this shortcoming, Cai et al. proposed a novel algorithm, called graph regularized non-negative matrices factorization (GNMF). Motivated by the topological structure of the GNMF-based method, we propose improved graph regularized non-negative matrix factorization (GNMF) to facilitate the display of geometric structure of data space. Robust manifold non-negative matrix factorization (RM-GNMF) is designed for cancer gene clustering, leading to an enhancement of the GNMF-based algorithm in terms of robustness. We combine the -norm NMF with spectral clustering to conduct the wide-ranging experiments on the three known datasets. Clustering results indicate that the proposed method outperforms the previous methods, which displays the latest application of the RM-GNMF-based method in cancer gene clustering.

Highlights

With the progressive implementation of human whole-genome and microarray technologies, it is possible to simultaneously observe the expressions of numerous genes in different tissue samples
In order to evaluate the performance of proposed RM-graph regularized non-negative matrix factorization (GNMF) algorithm, the clustering experiment was conducted on several gene expressions datasets of cancer patients
It can be found that the RM-GNMF-based method outperforms the original negative matrix factorization (NMF)-based method, while the RM-GNMF-based method achieves the best performance compared with the other three datasets

Summary

Introduction

With the progressive implementation of human whole-genome and microarray technologies, it is possible to simultaneously observe the expressions of numerous genes in different tissue samples. By analyzing gene expression data, genes with varying expressions in tissues and their relationships may be identified to figure out the pathogenic mechanism of cancers based on genetic changes [1]. Cancer classification based on gene expression data has become a hot research topic in bioinformatics. Due to the fact that the analysis of genome-wide expression patterns can provide unique perspectives into the structure of genetic networks, the clustering technique has been used to analyze gene expression data [2,3]. Cluster analysis is the most widespread statistical techniques for analyzing massive gene expression data. Its major task is to classify genes with similar expressions to discover groups of genes with identical features or similar biological functions, in order that people can acquire a deeper understanding about the essence of many biological phenomena such as gene functions, development, cancer, and pharmacology [4]

Methods

Results

Conclusion