What is principal component analysis?

By Markus Ringnér. Nature Biotechnology, 2008

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eDoctor: machine learning and the future of medicine (2018, methodology): “…of relevant features for use in model creation), pattern analysis and dimensionality reduction (reducing the number of random variables used by reducing data to its principal components) for complicated problems such as gene expression analysis, text analysis and computational chemistry [11, 12].”
Exploring patterns enriched in a dataset with contrastive principal component analysis (2018, background): “Researchers have noted that standard PCA is often ineffective at discovering subgroups within biological data, at least in part because “ dominant principal components … correlate with artifacts, ” 21 rather than with features that are of interest to the researcher.”
Integration strategies of multi-omics data for machine learning analysis (2021, methodology): “The most widely used FE method is Principal Component Analysis (PCA) [52].”