kill_cc: Regress out cell cycle effects

In kidcancerlab/rrrSingleCellUtils: A Set of Tools For Single Cell Analysis

Regress out cell cycle effects

Description

Regress out cell cycle effects

Usage

kill_cc(
  sobject,
  species = "human",
  cc_regress = "N",
  show_plots = TRUE,
  find_pcs = 20,
  use_pcs = 3,
  use_res = 0.5,
  method = "umap"
)

Arguments

sobject	Seurat object to be processed
species	What species does your data represent? "human" or "mouse"
cc_regress	If set to Y, the process with run without user input and will automatically proceed to cell cycle regression. If set to Ask, will prompt the user. If set to N no regression will be performed.
show_plots	Should the plots be printed?
find_pcs	Number of principal components to generate in the re-do PCA post-CC regression
use_pcs	Number of principal components to use in the post-regression dimensional reduction
use_res	Resolution to input to FindClusters
method	Type of dimensional reduction to use, currently supports either umap or tsne

Details

The kill_cc function will identify cell cycle components within a dataset. After an initial scoring using the Seurat CellCycleScoring function, the user will be shown a dimensional reduction plot with cells labeled by cell cycle. If indicated, the user can then trigger a process to regress out the effects of cell cycle within the dataset. The function will then proceed to re-do the PCA and jackstraw if needed, then show a dimensional reduction plot post-regression and retun the corrected Seurat object. Input must be a Seurat object that already has PCA and dimensional reduction data (umap or tsne) attached.

Value

A Seurat object

Examples

## Not run: 
load("~/analyses/roberts/dev/rrrSingleCellUtils/testData/test_cc.RData")
test <- kill_cc(os, use_pcs = 5, cc_regress = "Y")

## End(Not run)

kidcancerlab/rrrSingleCellUtils documentation built on April 17, 2025, 5:10 p.m.