Run PCA on matrix — run_pca_matrix • moiraine

Runs a Principal Component Analysis on an omics matrix, using the pcaMethods::pca() function.

Usage

run_pca_matrix(mat, n_pcs = 10, scale = "none", center = TRUE, method = NULL)

mat: Matrix of omics measurement, with features as rows and samples as columns.
n_pcs: numeric, number of Principal Components to compute. Default value is 10.
scale: character, type of scaling that should be applied to the dataset before running the PCA. Should be one of 'none', 'pareto', 'vector', 'uv' (see pcaMethods::pca()). Default value is 'none'.
center: boolean, should the dataset be centred prior to running the PCA? Default value is TRUE.
method: character, type of PCA that should be applied to the dataset. See pcaMethods::listPcaMethods(). for a list of available methods. Default value is 'svd' for datasets with no missing value, and 'nipals' for datasets with missing values.

A pcaMethods::pcaRes object containing the result from the PCA analysis. The attribute dataset_name specifies the name of the dataset analysed.