Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publicationready figures. PCA is performed via BiocSingular  users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in singlecell RNAseq (scRNAseq) and high dimensional mass cytometry data.
Package details 


Author  Kevin Blighe [aut, cre], AnnaLeigh Brown [ctb], Vincent Carey [ctb], Guido Hooiveld [ctb], Aaron Lun [aut, ctb] 
Bioconductor views  GeneExpression PrincipalComponent RNASeq SingleCell Transcription 
Maintainer  Kevin Blighe <kevin@clinicalbioinformatics.co.uk> 
License  GPL3 
Version  2.2.0 
URL  https://github.com/kevinblighe/PCAtools 
Package repository  View on Bioconductor 
Installation 
Install the latest version of this package by entering the following in R:

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