Low Rank Representation and Its Application in Bioinformatics

Background: Sparse representation has achieved tremendous success recently. Low-rank representation is one of the successful methods. It is aimed to capture underlying low-dimensional structures of high dimensional data and attracted much attention in the area of the pattern recognition and signal processing. Such successful applications were mainly to its effectiveness in exploring low-dimensional manifolds embedded in data, which can be naturally characterized by low rankness of the data matrix. Objective: In this paper, we review the theoretical and numerical models based on low rank representation and hope the review can attract more research in bioinformatics. Method: Low rank representation is particularly well suited to big data analysis in bioinformatics. The first reason is that the interested objects are naturally sparse, like copy number variations. The second reason is that there exist strong correlations among various modalities for the same object, like DNA, RNA and methylation. Results and Conclusion: Its applications in bioinformatics area, including mining of key genes subset, finding common patterns across various modalities and biomedical image analysis were categorically summarized.