Sequential Projection Pursuit Principal Component Analysis – Dealing with Missing Data Associated with New -Omics Technologies
Year: 2013
Type: article
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.
Source: BioTechniques
DOI: 10.2144/000113978
Institution: Pacific Northwest National Laboratory
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,415
Awards: G2287113261, Proteomics and Metabolomics Studies of Type 1 Diabetes, Proteomics and Metabolomics Studies of Type 1 Diabetes, G4161019194, G6558320416, Visual Analytics Software Environment for Proteomics Data Integration, Center for Application of Advanced Clinical Proteomic Technologies for Cancer, G7629298489
OpenAlex ID: W1995567536