Points of Significance: Principal component analysis

Abstract

Principal component analysis (PCA) simplifies the complexity in high-dimensional data while retaining trends and patterns. It does this by transforming the data into fewer dimensions, which act as summaries of features. High-dimensional data are very common in biology and arise when multiple features, such as expression of many genes, are measured for each… (More)
DOI: 10.1038/nmeth.4346

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Cite this paper

@article{Lever2017PointsOS, title={Points of Significance: Principal component analysis}, author={Jake Lever and Martin Krzywinski and Naomi Altman}, journal={Nature Methods}, year={2017}, volume={14}, pages={641-642} }