run_seurat_clustering: Seurat Clustering

In NWhitener/benchmarking: Benchmarking Tools For Single Cell RNA Sequencing Datasets

Seurat Clustering

Description

This functions runs the Seurat FindNeighbors and FindClusters function on a list of data sets. This functions assumes that genes are in rows and cells are in columns. The FindNeighbors reduction is set to "pca" by default and uses the first 10 dimensions. The FindClusters resolution is set to 0.5 by default, but is controllable by the user

Usage

run_seurat_clustering(
  dataList,
  reductionType = "pca",
  resolutionValue = 0.5,
  numberComponents = 10,
  seed = 1
)

Arguments

dataList	A list of data sets to be clustered
reductionType	The type of dimensionly reduced data that should be used for clustering, defaults to PCA
resolutionValue	The resolution for the Seurat clustering method, defaults to 0.5
numberComponents	the number of components to use, defaults to 10

Value

A data list with Seurat clustering completed

NWhitener/benchmarking documentation built on Oct. 23, 2023, 12:20 a.m.