Interactive QC panel for Seurat v5 objects — VisQCPanel • SeuratVisPro

Visualize key QC metrics with customizable thresholds and optional interactivity.

Usage

VisQCPanel(
  object,
  assay = NULL,
  genes_mt = "^MT-",
  genes_ribo = "^RPL|^RPS",
  group.by = NULL,
  interactive = FALSE,
  palette = "C",
  violin_width = 0.8,
  violin_alpha = 0.3,
  box_width = 0.3,
  box_alpha = 0.5
)

Arguments

object: A Seurat object; required.
assay: Assay name to use. Default: Seurat::DefaultAssay(object).
genes_mt: Regex for mitochondrial genes. Default: "^MT-".
genes_ribo: Regex for ribosomal genes (e.g., "^RPL|^RPS"). Default: NULL (skip ribosomal panel when NULL).
group.by: Column in object@meta.data used for grouping. Default: "seurat_clusters" if present, else identities or a single group.
interactive: Whether to return interactive plots via plotly::ggplotly. Default: FALSE.
palette: Viridis palette option for color/fill. Default: "C". Allowed: "A"–"H".
violin_width: Width of violin geoms. Default: 0.8.
violin_alpha: Alpha of violin geoms. Default: 0.3.
box_width: Width of boxplot geoms. Default: 0.3.
box_alpha: Alpha of boxplot geoms. Default: 0.5.

Value

A ggplot patchwork (or a plotly subplot when interactive=TRUE).

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

p <- VisQCPanel(
  obj,
  assay = NULL,
  genes_mt = "^MT-",
  genes_ribo = "^RPL|^RPS",
  group.by = "seurat_clusters",
  interactive = FALSE,
  palette = "C",
  violin_width = 0.8,
  violin_alpha = 0.3,
  box_width = 0.3,
  box_alpha = 0.5)
p