Principal Component Analysis (PCA)

Perform principal component analysis on the expression data. The function uses prcomp() to perform PCA and broom::tidy() to tidy the results. If scale = TRUE, constant variables (zero variance) will be removed before PCA.

Usage

gly_pca(exp, center = TRUE, scale = TRUE, add_info = TRUE, ...)

Arguments

exp

A glyexp::GlycomicSE() or glyexp::GlycoproteomicSE() object, or another SummarizedExperiment containing an expression matrix and sample information.

center

A logical indicating whether to center the data. Default is TRUE.

scale

A logical indicating whether to scale the data. Default is TRUE.

add_info

A logical value. If TRUE (default), sample and variable information from the experiment will be added to the result tibbles. If FALSE, only the PCA results are returned.

...

Additional arguments passed to prcomp().

Value

A list containing:

• tidy_result: A list of tibbles with PCA results:

  • samples: PCA scores for each sample containing the following columns:

    • sample: Sample name

    • PC: Principal component name (PC1, PC2, etc.)

    • value: Score value for the principal component

  • variables: PCA loadings for each variable containing the following columns:

    • variable: Variable name

    • PC: Principal component name (PC1, PC2, etc.)

    • value: Loading value for the principal component

  • eigenvalues: PCA eigenvalues containing the following columns:

    • PC: Principal component name (PC1, PC2, etc.)

    • std.dev: Standard deviation

    • percent: Percentage of variance explained

    • cumulative: Cumulative percentage of variance explained

• raw_result: The raw prcomp object from stats::prcomp()

• meta_data: A list containing metadata from the input experiment

Details

The function performs log transformation on the expression data (log(x + 1)) before PCA analysis.

Required packages

This function only uses base R packages and does not require additional dependencies.

See also

stats::prcomp()