FindClusterSweep: Run common single-cell RNA-seq clustering algorithms as...

In mgildea87/CVRCFunc: Functions for CVRC bioinformatics

Run common single-cell RNA-seq clustering algorithms as implemented in Seurat across a range of resolution values and compute common clustering metrics. This function assumes a KNN graph already exists in the specified assay. Run seurat's FindNeighbors before this function.

Description

Run common single-cell RNA-seq clustering algorithms as implemented in Seurat across a range of resolution values and compute common clustering metrics. This function assumes a KNN graph already exists in the specified assay. Run seurat's FindNeighbors before this function.

Usage

FindClusterSweep(
  seurat,
  assay = "RNA",
  resolutions = c(0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6),
  algorithm = 1,
  conda,
  pca_dim = c(1:30),
  reduction = "pca",
  plot_reduction = "umap",
  file_name = "FindClusterSweep_plots"
)

Arguments

seurat	A seurat object
assay	seurat assay e.g. 'RNA'
resolutions	Vector of clustering resolutions
algorithm	Seurat FindClusters algorithm parameter. From Seurat: 'Algorithm for modularity optimization 1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm. Leiden requires the leidenalg python.'
conda	If applicable, path to conda environment. Required for Leiden algorithm = 4
pca_dim	vector of dimensions to use for computing silheoutte scores. default = 0,30
reduction	reduction to use for computing silhouette scores. default = 'pca'
plot_reduction	reduction to plot silhouette scores. default = 'umap'
file_name	plot .pdf file name. default = 'FindClusterSweep_plots'

Value

A seurat object with clustering. .pdf document with a series of clustering related plots

mgildea87/CVRCFunc documentation built on Nov. 9, 2024, 7:39 p.m.