runPCA: Principal Component Analysis (PCA)

In dibbelab/gficf: Gene Frequency - Inverse Cell Frequency

Principal Component Analysis (PCA)

Description

Reduce dimensionality of the single cell dataset using Principal Component Analysis (PCA)

Usage

runPCA(
  data,
  dim = NULL,
  var.scale = F,
  centre = F,
  randomized = T,
  seed = 180582,
  use.odgenes = F,
  n.odgenes = NULL,
  plot.odgenes = F
)

Arguments

data	list; GFICF object
dim	integer; Number of dimension which to reduce the dataset.
var.scale	logical; Rescale gficf scores for adjusted variance like in pagoda2 (highly experimental!).
centre	logical; Centre gficf scores before applying reduction (increase separation).
randomized	logical; Use randomized (faster) version for matrix decomposition (default is TRUE).
seed	integer; Initial seed to use.
use.odgenes	boolean; Use significant overdispersed genes respect to ICF values (highly experimental!).
n.odgenes	integer; Number of overdispersed genes to use (highly experimental!). A good choise seems to be usually between 1000 and 3000.
plot.odgenes	boolean; Show significant overdispersed genes respect to ICF values.

Value

The updated gficf object.

The updated gficf object.

dibbelab/gficf documentation built on Nov. 2, 2022, 2:28 a.m.