runPCA: Principal components analysis
In BiocSingular: Singular Value Decomposition for Bioconductor Packages

Description Usage Arguments Details Value Author(s) See Also Examples

Perform a principal components analysis (PCA) on a target matrix with a specified SVD algorithm.

runPCA(x, ...)

## S4 method for signature 'ANY'
runPCA(x, rank, center=TRUE, scale=FALSE, get.rotation=TRUE,
    get.pcs=TRUE, ...)

`x`	A numeric matrix-like object with samples as rows and variables as columns.
`rank`	Integer scalar specifying the number of principal components to retain.
`center`	A logical scalar indicating whether columns of `x` should be centered before the PCA is performed. Alternatively, a numeric vector of length `ncol(x)` containing the value to subtract from each column of `x`.
`scale`	A logical scalar indicating whether columns of `x` should be scaled to unit variance before the PCA is performed. Alternatively, a numeric vector of length `ncol(x)` containing the scaling factor for each column of `x`.
`get.rotation`	A logical scalar indicating whether rotation vectors should be returned.
`get.pcs`	A logical scalar indicating whether the principal component scores should be returned.
`...`	For the generic, this contains arguments to pass to methods upon dispatch. For the `ANY` method, this contains further arguments to pass to `runSVD`. This includes `BSPARAM` to specify the algorithm that should be used, and `BPPARAM` to control parallelization.

This function simply calls runSVD and converts the results into a format similar to that returned by prcomp.

The generic is exported to allow other packages to implement their own runPCA methods for other x objects, e.g., scater for SingleCellExperiment inputs.

A list is returned containing:

sdev, a numeric vector of length rank containing the standard deviations of the first rank principal components.
rotation, a numeric matrix with rank columns and nrow(x) rows, containing the first rank rotation vectors. This is only returned if get.rotation=TRUE.
x, a numeric matrix with rank columns and ncol(x) rows, containing the scores for the first rank principal components. This is only returned if get.pcs=TRUE.

Aaron Lun

runSVD for the underlying SVD function.

?BiocSingularParam for details on the algorithm choices.

1 2	a <- matrix(rnorm(100000), ncol=20) str(out <- runPCA(a, rank=10))

List of 3
 $ sdev    : num [1:10] 1.06 1.04 1.04 1.03 1.03 ...
 $ rotation: num [1:20, 1:10] 0.0259 0.2733 0.2581 -0.1309 -0.1749 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr [1:10] "PC1" "PC2" "PC3" "PC4" ...
 $ x       : num [1:5000, 1:10] -2.049 -1.38 0.896 2.226 -0.555 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr [1:10] "PC1" "PC2" "PC3" "PC4" ...