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  "Description": "Principal Component Analysis (PCA) is a very powerful\ntechnique that has wide applicability in data science,\nbioinformatics, and further afield. It was initially developed\nto analyse large volumes of data in order to tease out the\ndifferences/relationships between the logical entities being\nanalysed. It extracts the fundamental structure of the data\nwithout the need to build any model to represent it. This\n'summary' of the data is arrived at through a process of\nreduction that can transform the large number of variables into\na lesser number that are uncorrelated (i.e. the 'principal\ncomponents'), while at the same time being capable of easy\ninterpretation on the original data. PCAtools provides\nfunctions for data exploration via PCA, and allows the user to\ngenerate publication-ready figures. PCA is performed via\nBiocSingular - users can also identify optimal number of\nprincipal components via different metrics, such as elbow\nmethod and Horn's parallel analysis, which has relevance for\ndata reduction in single-cell RNA-seq (scRNA-seq) and high\ndimensional mass cytometry data.",
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