pca: Principal Component Analysis (PCA) on a...

In GaelleLefort/ASICS: Automatic Statistical Identification in Complex Spectra

Description

Perform a PCA with the function of the ropls package on a SummarizedExperiment object obtained from the formatForAnalysis function

Usage

pca(
  analysis_data,
  scale.unit = TRUE,
  type.data = "quantifications",
  condition = NULL
)

Arguments

analysis_data	A SummarizedExperiment object obtained from the formatForAnalysis function.
scale.unit	Logical. If TRUE, data are scaled to unit variance prior PCA.
type.data	Type of data used for the analysis (e.g., "quantifications", "buckets"...). Default to "quantifications".
condition	The name of the design variable (two level factor) specifying the groups, if one is available. Default to NULL, no group provided.

Value

A S4 object of class AnalysisResults containing PCA results.

See Also

AnalysisResults

Examples

# Import quantification results
if (require("ASICSdata", quietly = TRUE)) {
  quantif_path <- system.file("extdata", "results_ASICS.txt",
                              package = "ASICSdata")
  quantification <- read.table(quantif_path, header = TRUE, row.names = 1)

  # Create object for analysis and remove features with more than 25% of
  # zeros
  analysis_obj <- formatForAnalysis(quantification, zero.threshold = 25)
  res_pca <- pca(analysis_obj)
}

GaelleLefort/ASICS documentation built on July 19, 2020, 2:08 p.m.