Run Seurat clustering and plot 2D embedding

Performs Seurat clustering on an expression matrix and generates a 2D embedding visualization (UMAP or t-SNE). Useful for identifying subclones within patient tumor samples.

Usage

plot_seurat_clustering(
  method = c("umap", "tsne"),
  expression_matrix,
  min_cells = 3,
  min_features = 200,
  nfeatures = 2000,
  dims = 10,
  resolution = 0.8,
  seed = 1
)

Arguments

method

Character. Dimensionality reduction method. One of "umap" (default) or "tsne".

expression_matrix

Matrix. Gene expression matrix (genes as rows, cells as columns).

min_cells

Integer. Minimum cells per feature. Default = 3.

min_features

Integer. Minimum features per cell. Default = 200.

nfeatures

Integer. Number of variable features. Default = 2000.

dims

Integer. Number of PCA dimensions for clustering. Default = 10.

resolution

Numeric. Clustering resolution. Default = 0.8.

seed

Integer. Random seed. Default = 1.

Value

A list containing:

seurat_object

Seurat object with clustering results

embedding_plot

ggplot 2D embedding visualization

cluster_ids

Named vector of cluster IDs per cell

Examples

if (FALSE) { # \dontrun{
  result <- plot_seurat_clustering(patient_expression)
  result$embedding_plot
  result$cluster_ids
} # }