期刊
CANCER INFORMATICS
卷 17, 期 -, 页码 -出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/1176935118771082
关键词
Dimension reduction; Bayes rule; Auer-Gervini; broken stick; randomization-based procedure
资金
- NIH/NCI [P30 CA016058, R01 CA182905]
Principal component analysis (PCA) is one of the most common techniques in the analysis of biological data sets, but applying PCA raises 2 challenges. First, one must determine the number of significant principal components (PCs). Second, because each PC is a linear combination of genes, it rarely has a biological interpretation. Existing methods to determine the number of PCs are either subjective or computationally extensive. We review several methods and describe a new R package, PCDimension, that implements additional methods, the most important being an algorithm that extends and automates a graphical Bayesian method. Using simulations, we compared the methods. Our newly automated procedure is competitive with the best methods when considering both accuracy and speed and is the most accurate when the number of objects is small compared with the number of attributes. We applied the method to a proteomics data set from patients with acute myeloid leukemia. Proteins in the apoptosis pathway could be explained using 6 PCs. By clustering the proteins in PC space, we were able to replace the PCs by 6 biological components, 3 of which could be immediately interpreted from the current literature. We expect this approach combining PCA with clustering to be widely applicable.
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