关键词: 非负矩阵分解, 微阵列数据, 分类分析, 聚类发现

Abstract:

A typical representation of microarray technologies is DNA microarray, which has ability to simultaneously measure the expression levels of all genes in genome due to its property of highthroughput. One of the main objectives of microarrays assay is gene expression pattern discovery, that is, not only the discovery of gene clusters where genes have similar functions or relative biological process, but also the discovery of sample subtypes which possess the intrinsic features, such as cancer subtypes. Nonnegative matrix factorization is an unsupervised, nonorthogonal, localbased representation methodology used into microarrays data analysis, especially in classification analysis and clustering discovery. The typical algorithm and some improved algorithms of NMF are introduced, and the biological annotation of factorization, the assessment of classification outcomes and the existing implementations basedon NMF are systematically summarized. Finally, the performance of NMF in recent microarray experiments is given.

Key words: non-negative matrix factorization;microarray data;classification analysis;clustering discovery