pca: Principal Component Analysis (PCA) on a...
In ASICS: Automatic Statistical Identification in Complex Spectra

Description Usage Arguments Value See Also Examples

View source: R/analysis_on_quantification.R

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

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

`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.

A S4 object of class AnalysisResults containing PCA results.

AnalysisResults

# 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)
}