Learning the local molecular pattern of Alzheimer's disease by non-negative matrix factorization

Abstract

Gene microarray technology is an effective tool to monitor simultaneous activity of multiple cellular pathways from thousands of genes in a single chip. Many clustering methods have been developed to identify groups of genes or experimental conditions that exhibit similar expression patterns from gene expression data, such as hierarchical clustering, k-means, and self-organizing maps (SOM). The limitations of these clustering algorithms are: they group genes (or conditions) based on global similarities in their expression profiles and only assign each gene to a single cluster. In this work we present a biclustering method nonnegtive matrix factorization (NMF) to avoid the above drawbacks and discover the local molecular pattern from gene expression datasets of Alzheimer's disease (AD). NMF can be applied to reduce the dimensionality of the data and describe the data as a positive linear combination of a reduced number of factors. By applying a sparseness enforcement variable into classical NMF, the more local structures with meaningful biological information inherent in the data are captured by clustering genes and samples simultaneously, and the classification of samples is well improved. The analysis and discussion of the identified local structures demonstrated that they related many pathways which play a prominent role in AD and the activation patterns to AD phenotypes.

NMF-mGPU: non-negative matrix factorization on multi-GPU systems

“…One of the most popular applications of NMF in Bioinformatics is the Gene-expression Analysis [ 6 , 7 ]. It is based on the Microarray Technology , which is a powerful method able to monitor the expression level of thousands of genes, or even whole genomes, in a single experiment [ 44 ].…”

“…NMF decomposes a large input dataset into a small set of highly interpretable and additive parts. This property has centered the attention of scientists for a wide range of applications in Bioinformatics, such as gene-expression analysis [ 6 , 7 ], scientific literature mining [ 8 ], neuroscience [ 9 ], and several -omics techniques [ 10 , 11 ]. A good review of some of them can be found in [ 12 ].…”