Singular value decomposition for genome-wide expression data processing and modeling.

@article{Alter2000SingularVD,
  title={Singular value decomposition for genome-wide expression data processing and modeling.},
  author={Orly Alter and Patrick O. Brown and David Botstein},
  journal={Proceedings of the National Academy of Sciences of the United States of America},
  year={2000},
  volume={97 18},
  pages={10101-6}
}
We describe the use of singular value decomposition in transforming genome-wide expression data from genes x arrays space to reduced diagonalized "eigengenes" x "eigenarrays" space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across… CONTINUE READING