title: Bioinformatics tools for cancer metabolomics.
abstract: It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages.
authors.forename: Grigoriy
authors.initials: G
authors.lastname: Blekherman
authors.forename: Reinhard
authors.initials: R
authors.lastname: Laubenbacher
authors.forename: Diego F
authors.initials: DF
authors.lastname: Cortes
authors.forename: Pedro
authors.initials: P
authors.lastname: Mendes
authors.forename: Frank M
authors.initials: FM
authors.lastname: Torti
authors.forename: Steven
authors.initials: S
authors.lastname: Akman
authors.forename: Suzy V
authors.initials: SV
authors.lastname: Torti
authors.forename: Vladimir
authors.initials: V
authors.lastname: Shulaev