Microarray Data Analysis of Yeast Data Using Sparse Non-negative Matrix Factorization

Microarray expression data contains observations from thousands of genes across hundreds of samples. To extract meaningful information from these large datasets, the dimensionality reduction technique known as non-negative matrix factorization, or NMF, is introduced. This tool transforms the data and makes it more amenable to clustering. NMF was applied to a yeast microarray dataset. Three main clusters were discovered, corresponding to three distinct metabolic cycles. The data were also clustered using the k-means algorithm, and the clustering result was highly similar to that obtained by NMF.