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Build a negative-control panel from principal components

Source: R/negative_controls.R
build_w_pcs.Rd

Computes the top n_pcs principal components of a features x samples matrix (typically residualized with residualize_matrix()) and returns the sample scores as a W panel (PCs x samples) in the orientation expected by iconic_data(). Uses irlba for fast truncated PCA when available, falling back to stats::prcomp().

Usage

build_w_pcs(x, n_pcs = 20, scale_features = FALSE, prefix = "PC")

Arguments

x

Numeric matrix, features x samples (e.g. residualized M-values or predicted expression).

n_pcs

Integer: number of PCs to return. Default 20.

scale_features

Logical: scale features to unit variance before PCA. Default FALSE (appropriate for M-values); use TRUE for predicted-expression panels where feature scales differ.

prefix

Character: row-name prefix for the PCs. Default "PC".

Value

A list with:

W

numeric matrix, PCs x samples, suitable as the W argument of iconic_data().

variance_explained

numeric vector: percent of total variance explained by each returned PC.

Examples

x <- matrix(rnorm(500 * 60), nrow = 500,
            dimnames = list(paste0("f", 1:500), paste0("S", 1:60)))
w <- build_w_pcs(x, n_pcs = 5)
dim(w$W)
#> [1]  5 60
w$variance_explained
#> [1] 3.027690 2.944088 2.717830 2.669895 2.621035