pca: Principal Component Analysis
In CellTrails: Reconstruction, visualization and analysis of branching trajectories

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

Performs principal component analysis by spectral decomposition of a covariance or correlation matrix

1	pca(sce, do_scaling = TRUE, design = NULL)

`sce`	`SingleCellExperiment` object
`do_scaling`	FALSE = covariance matrix, TRUE = correlation matrix
`design`	A numeric matrix describing the factors that should be blocked

The calculation is done by a spectral decomposition of the (scaled) covariance matrix of the trajectory features as defined in the SingleCellExperiment object. Features with zero variance get automatically removed. Please note that this methods only uses the set of defined trajectory features in a SingleCellExperiment object; spike-in controls are ignored and are not listed as trajectory features.

To account for systematic bias in the expression data (e.g., cell cycle effects), a design matrix can be provided for the learning process. It should list the factors that should be blocked and their values per sample. It is suggested to construct a design matrix with model.matrix.

A list object containing the following components:

`components`	Principal components
`eigenvalues`	Variance per component
`variance`	Fraction of variance explained by each component
`loadings`	Loading score for each feature

Daniel C. Ellwanger

SingleCellExperiment model.matrix

# Example data
data(exSCE)

# Principal component analysis
res <- pca(exSCE)

# Find relevant number of principal components
d <- findSpectrum(res$eigenvalues, frac=20)

barplot(res$variance[d] * 100, ylab="Variance (%)",
        names.arg=colnames(res$components)[d], las=2)
plot(res$component, xlab="PC1", ylab="PC2")