RunQuantileNorm: Run quantile_norm on a Seurat object

In satijalab/seurat-wrappers: Community-Provided Methods and Extensions for the Seurat Object

Run quantile_norm on a Seurat object

Description

Run quantile_norm on a Seurat object

Usage

RunQuantileNorm(
  object,
  split.by = "orig.ident",
  reduction = "iNMF_raw",
  reduction.name = "iNMF",
  reduction.key = "iNMF_",
  quantiles = 50,
  ref_dataset = NULL,
  min_cells = 20,
  knn_k = 20,
  dims.use = NULL,
  do.center = FALSE,
  max_sample = 1000,
  eps = 0.9,
  refine.knn = TRUE,
  ...
)

Arguments

object	A merged Seurat object
split.by	Attribute for splitting, defaults to "orig.ident"
reduction.name	Name to store resulting DimReduc object as
reduction.key	Key for resulting DimReduc
quantiles	Number of quantiles to use for quantile normalization (default 50).
ref_dataset	Name of dataset to use as a "reference" for normalization. By default, the dataset with the largest number of cells is used.
min_cells	Minimum number of cells to consider a cluster shared across datasets (default 20)
knn_k	Number of nearest neighbors for within-dataset knn graph (default 20).
dims.use	Indices of factors to use for shared nearest factor determination (default 1:ncol(H[[1]])).
do.center	Centers the data when scaling factors (useful for less sparse modalities like methylation data). (default FALSE)
max_sample	Maximum number of cells used for quantile normalization of each cluster and factor. (default 1000)
eps	The error bound of the nearest neighbor search. (default 0.9) Lower values give more accurate nearest neighbor graphs but take much longer to computer.
refine.knn	whether to increase robustness of cluster assignments using KNN graph.(default TRUE)
...	Arguments passed to other methods

Value

A Seurat object with embeddings from quantile_norm stored as a DimReduc object with name reduction.name (key set to reduction.key)

See Also

quantile_norm

satijalab/seurat-wrappers documentation built on April 10, 2024, 3:25 p.m.