runRPCA: Run Robust Prinicipal Component Analysis

In NMikolajewicz/scMiko: scRNAseq analysis functions. Developed using Seurat framework.

Run Robust Prinicipal Component Analysis

Description

Run a robust PCA (rPCA) dimensionality reduction on single-cell seurat object.

Usage

runRPCA(
  object,
  assay = NULL,
  features = NULL,
  npcs = 50,
  maxpcs = 50,
  reduction.key = "RPC_",
  reduction.name = "rpca",
  seed.use = 42,
  verbose = T,
  method = c("hubert", "robpca", "fasthcs", "pcal"),
  maxdir = 100,
  signflip = T,
  ...
)

Arguments

object	Seurat object
assay	Name of Assay rPCA is being run on
features	Features to compute PCA on. If features=NULL, PCA will be run using scaled features for the Assay. Note that the features must be present in the scaled data. Any requested features that are not scaled or have 0 variance will be dropped, and the PCA will be run using the remaining features.
npcs	Total Number of PCs to compute and store (50 by default)
maxpcs	Max Number of PCs to compute and store (50 by default)
reduction.name	dimensional reduction name, rpca by default
seed.use	Set a random seed. By default, sets the seed to 42. Setting NULL will not set a seed.
verbose	Print progress. Default is TRUE.
method	Robust PCA method. default is "hubert".
maxdir	maximal number of random directions to use for computing the outlyingness of the data points. Default is maxdir=100.
signflip	a logical value indicating wheather to try to solve the sign indeterminancy of the loadings - ad hoc approach setting the maximum element in a singular vector to be positive. Default is signflip = TRUE
...	additional parameters passed to rPCA methods.
reductions.key	dimensional reduction key, specifies the string before the number for the dimension names. RPC by default

Value

Seurat object

Author(s)

Nicholas Mikolajewicz

See Also

PcaHubert

NMikolajewicz/scMiko documentation built on June 28, 2023, 1:41 p.m.