Visualization and classification of microarray gene data by nonnegtive matrix factorization

Gene microarray technology is an effective tool to collect the expression levels of thousands of genes from a single array. However, exploitation of the huge amount of data generated by microarrays is difficult because they are complex and noisy high-dimensional data. In this work, we present a biclustering method nonnegtive matrix factorization (NMF) to reduce the dimensionality of the data and discover the underlying biological process from gene expression data of Alzheimer´s disease (AD). The simulation results show that the reduction of dimension and identification of informatively biological process are useful for both visualization and analyzing of such high-throughput gene dataset.