Nonnegative matrix factorization: An analytical and interpretive tool in computational biology

Research output: Contribution to journalReview articlepeer-review

305 Scopus citations


In the last decade, advances in high-throughput technologies such as DNA microarrays have made it possible to simultaneously measure the expression levels of tens of thousands of genes and proteins. This has resulted in large amounts of biological data requiring analysis and interpretation. Nonnegative matrix factorization (NMF) was introduced as an unsupervised, parts-based learning paradigm involving the decomposition of a nonnegative matrix V into two nonnegative matrices, W and H, via a multiplicative updates algorithm. In the context of a pxn gene expression matrix V consisting of observations on p genes from n samples, each column of W defines a metagene, and each column of H represents the metagene expression pattern of the corresponding sample. NMF has been primarily applied in an unsupervised setting in image and natural language processing. More recently, it has been successfully utilized in a variety of applications in computational biology. Examples include molecular pattern discovery, class comparison and prediction, cross-platform and cross-species analysis, functional characterization of genes and biomedical informatics. In this paper, we review this method as a data analytical and interpretive tool in computational biology with an emphasis on these applications.

Original languageEnglish
Article numbere1000029
Pages (from-to)e1000029
JournalPLoS Computational Biology
Issue number7
StatePublished - Jul 2008


  • Artificial Intelligence
  • Computational Biology/methods
  • Gene Expression Profiling/methods
  • Oligonucleotide Array Sequence Analysis/methods
  • Pattern Recognition, Automated/methods
  • Systems Integration


Dive into the research topics of 'Nonnegative matrix factorization: An analytical and interpretive tool in computational biology'. Together they form a unique fingerprint.

Cite this