Generate Seurat object for demonstrations

Generate a Seurat object with basic preprocessing (DefaultAssay, PercentageFeatureSet, NormalizeData, FindVariableFeatures, ScaleData, RunPCA, FindNeighbors, FindClusters, RunUMAP).

Usage

SeuratVisProExample(
  n_cells = 300,
  n_genes = 1000,
  n_clusters = 10,
  seed = 123,
  genes_mt = "^MT-",
  neighbor_dims = 10,
  cluster_res = 0.5,
  umap_dims = 10,
  spatial = FALSE
)

Arguments

n_cells

Number of cells. Default: 300.

n_genes

Number of genes. Default: 1000.

n_clusters

Number of clusters. Default: 10.

seed

Random seed for reproducibility. Default: 123.

genes_mt

Regex for mitochondrial genes. Default: "^MT-".

neighbor_dims

Neighbor graph dimensions used in FindNeighbors. Default: 10.

cluster_res

Cluster resolution used in FindClusters. Default: 0.5.

umap_dims

UMAP dimensions used in RunUMAP. Default: 10.

spatial

Whether to add synthetic spatial coordinates x,y to meta.data. Default: FALSE.

Value

A Seurat object with basic preprocessing steps applied.

Author

benben-miao

Examples

obj <- SeuratVisProExample(
    n_cells = 300,
    n_genes = 1000,
    n_clusters = 10,
    seed = 123,
    genes_mt = "^MT-",
    neighbor_dims = 10,
    cluster_res = 0.5,
    umap_dims = 10,
    spatial = FALSE)
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#> 
#> Number of nodes: 300
#> Number of edges: 4508
#> 
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.9485
#> Number of communities: 10
#> Elapsed time: 0 seconds
#> Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
#> To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
#> This message will be shown once per session

Seurat::DimPlot(obj, group.by = "cluster")