biplot                  Draw a bi-plot, comparing 2 selected principal
                        components / eigenvectors.
chooseGavishDonoho      Choosing PCs with the Gavish-Donoho method
chooseMarchenkoPastur   Choosing PCs with the Marchenko-Pastur limit
eigencorplot            Correlate principal components to continuous
                        variable metadata and test significancies of
                        these.
findElbowPoint          Find the elbow point in the curve of variance
                        explained by each successive PC. This can be
                        used to determine the number of PCs to retain.
getComponents           Return the principal component labels for an
                        object of class 'pca'.
getLoadings             Return component loadings for principal
                        components from an object of class 'pca'.
getVars                 Return the explained variation for each
                        principal component for an object of class
                        'pca'.
pairsplot               Draw multiple bi-plots.
parallelPCA             Perform Horn's parallel analysis to choose the
                        number of principal components to retain.
pca                     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'), whilst 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 publication-ready figures. PCA is
                        performed via BiocSingular - users can also
                        identify optimal number of principal component
                        via different metrics, such as elbow method and
                        Horn's parallel analysis, which has relevance
                        for data reduction in single-cell RNA-seq
                        (scRNA-seq) and high dimensional mass cytometry
                        data.
plotloadings            Plot the component loadings for selected
                        principal components / eigenvectors and label
                        variables driving variation along these.
screeplot               Draw a SCREE plot, showing the distribution of
                        explained variance across all or select
                        principal components / eigenvectors.
