RunPCA: Run Principal Component Analysis

In satijalab/seurat: Tools for Single Cell Genomics

Run Principal Component Analysis

Description

Run a PCA dimensionality reduction. For details about stored PCA calculation parameters, see PrintPCAParams.

Usage

RunPCA(object, ...)

## Default S3 method:
RunPCA(
  object,
  assay = NULL,
  npcs = 50,
  rev.pca = FALSE,
  weight.by.var = TRUE,
  verbose = TRUE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.key = "PC_",
  seed.use = 42,
  approx = TRUE,
  ...
)

## S3 method for class 'Assay'
RunPCA(
  object,
  assay = NULL,
  features = NULL,
  npcs = 50,
  rev.pca = FALSE,
  weight.by.var = TRUE,
  verbose = TRUE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.key = "PC_",
  seed.use = 42,
  ...
)

## S3 method for class 'Seurat'
RunPCA(
  object,
  assay = NULL,
  features = NULL,
  npcs = 50,
  rev.pca = FALSE,
  weight.by.var = TRUE,
  verbose = TRUE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.name = "pca",
  reduction.key = "PC_",
  seed.use = 42,
  ...
)

Arguments

object	An object
...	Arguments passed to other methods and IRLBA
assay	Name of Assay PCA is being run on
npcs	Total Number of PCs to compute and store (50 by default)
rev.pca	By default computes the PCA on the cell x gene matrix. Setting to true will compute it on gene x cell matrix.
weight.by.var	Weight the cell embeddings by the variance of each PC (weights the gene loadings if rev.pca is TRUE)
verbose	Print the top genes associated with high/low loadings for the PCs
ndims.print	PCs to print genes for
nfeatures.print	Number of genes to print for each PC
reduction.key	dimensional reduction key, specifies the string before the number for the dimension names. PC by default
seed.use	Set a random seed. By default, sets the seed to 42. Setting NULL will not set a seed.
approx	Use truncated singular value decomposition to approximate PCA
features	Features to compute PCA on. If features=NULL, PCA will be run using the variable 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.
reduction.name	dimensional reduction name, pca by default

Value

Returns Seurat object with the PCA calculation stored in the reductions slot

satijalab/seurat documentation built on May 11, 2024, 4:04 a.m.