Generate a QC heatmap for Pan-human Azimuth cell type annotations based on the top markers for each cluster

Generate a QC heatmap for Pan-human Azimuth cell type annotations based on the top markers for each cluster

Usage

make_QC_heatmap(
  seurat_obj,
  group.by = NULL,
  cells.order = NULL,
  n_downsample = 200,
  save_folder_path = NULL,
  logfc_cutoff = log(2),
  n_markers = 10,
  text.size = 5,
  text.angle = 90,
  min.size = NULL,
  max.size = NULL,
  min.pct = 0.1,
  reorder = TRUE,
  switch_id = NULL,
  identity = ""
)

Arguments

• seurat_obj:

  Seurat object to plot

• group.by:

  Column name in the metadata to use for grouping

• cells.order:

  Optional vector of cell names to specify the order of cells in the heatmap

• n_downsample:

  Number of cells to downsample to for each cluster (default 200)

• save_folder_path:

  Optional folder path to save the heatmap

• logfc_cutoff:

  Log fold change cutoff for selecting markers (default log(2))

• n_markers:

  Number of top markers to select for each cluster (default 10)

• text.size:

  Size of the text labels in the heatmap (default 5)

• text.angle:

  Angle of the text labels in the heatmap (default 90)

• min.size:

  Minimum number of cells in a cluster to include in the heatmap

• max.size:

  Maximum number of cells in a cluster to include in the heatmap

• min.pct:

  Minimum percentage of cells expressing a gene to consider it as a marker (default 0.1)

• reorder:

  Whether to reorder the clusters based on hierarchical clustering of average expression profiles (default TRUE)

• switch_id:

  Optional column name in the metadata to switch the Idents to before plotting

• identity:

  Optional string to append to the saved heatmap filename

Value

A ggplot object representing the QC heatmap