run_PCA: Principal Components Analysis

In metabolomicsR: Tools for Metabolomics Data

Principal Components Analysis

Description

Performs a principal components analysis on the Metabolite object.

Usage

run_PCA(
  object,
  nPCs = 10,
  impute_method = "half-min",
  log = TRUE,
  scale = TRUE,
  addPC = TRUE
)

Arguments

object	A Metabolite object.
nPCs	Number of principal components to be calculated. Default value 10.
impute_method	Imputation method, the default method is half the minimum value (‘half-min') of the metabolite. Currently support ’half-min', "median", "mean", "zero". 'NULL' without imputation.
log	Performs natural logarithm transformation before PCA analysis.
scale	scale feature in the PCA calculation.
addPC	If TRUE, merge PCs with '@sampleData' and return the 'object', else return 'PC'.

Value

A list of PCs and variances explained.

Examples

data(df_plasma)
d <- run_PCA(df_plasma)

metabolomicsR documentation built on April 29, 2022, 9:05 a.m.