title: Sequential projection pursuit principal component analysis--dealing with missing data associated with new -omics technologies.
abstract: Principal Component Analysis (PCA) is a common exploratory tool used to evaluate large complex data sets. The resulting lower-dimensional representations are often valuable for pattern visualization, clustering, or classification of the data. However, PCA cannot be applied directly to many -omics data sets generated by newer technologies such as label-free mass spectrometry due to large numbers of non-random missing values. Here we present a sequential projection pursuit PCA (sppPCA) method for defining principal components in the presence of missing data. Our results demonstrate that this approach generates robust and informative low-dimensional data representations compared to commonly used imputation approaches.
authors.affiliation: Computational Biology & Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA.
authors.forename: Bobbie-Jo M
authors.initials: BJ
authors.lastname: Webb-Robertson
authors.forename: Melissa M
authors.initials: MM
authors.lastname: Matzke
authors.forename: Thomas O
authors.initials: TO
authors.lastname: Metz
authors.forename: Jason E
authors.initials: JE
authors.lastname: McDermott
authors.forename: Hyunjoo
authors.initials: H
authors.lastname: Walker
authors.forename: Karin D
authors.initials: KD
authors.lastname: Rodland
authors.forename: Joel G
authors.initials: JG
authors.lastname: Pounds
authors.forename: Katrina M
authors.initials: KM
authors.lastname: Waters
mesh_terms: D000818:Animals; D002853:Chromatography, Liquid / Q000379:methods; D030562:Databases, Protein; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D055432:Metabolomics / Q000379:methods; D025341:Principal Component Analysis; D040901:Proteomics / Q000379:methods*