multipca: Principal component analysis

In jordirofes/OmniOmics: Analysis of metabolomics (LC-MS) and transcriptomics (Microarray) data

Principal component analysis

Description

Calculates the PCA for a SummarizedExperiment or ExpressionSet

Usage

multipca(features, prefuns, method, groupvar, qcname, scale = TRUE, ...)

Arguments

features	An ExpressionSet or SummarizedExperiment object
prefuns	An option string vector indicating pre-processing functions applied to the features before the PCA. Recommended for metabolomics data to impute missing values. Pre-processing functions available are "pqn" normalization, "sum" normalization, "glog" transformation and "mvImp" multivariate imputation
method	A string indicating the multivariate imputation method. Usually "knn" or "rf"
groupvar	A numeric or string indicating the variable from the phenodata with the QC samples to apply the pre-processing functions that require QC.
qcname	The QC name from the groupvar
scale	Boolean indicating if the pca should be scaled or not.

Value

A prcomp object

Author(s)

Jordi Rofes Herrera

jordirofes/OmniOmics documentation built on Nov. 22, 2022, 5:46 a.m.