Plot Mean Expression Heatmap

Displays a heatmap of median marker/gene expression across clusters or cell types. Shows the expression patterns that define each population, helping to validate cluster annotations and identify marker genes. The heatmap is computed on-the-fly from the current active annotations and expression data.

Usage

plotHeatmap(x, ...)

# S3 method for class 'TDRObj'
plotHeatmap(x, .id.from = "clustering", ...)

Arguments

x

A TDRObj, Seurat, SingleCellExperiment, or HDF5AnnData (anndataR) object processed through get.graph().

...

Additional arguments passed to methods.

.id.from

Character: "clustering" or "celltyping" (default "clustering"). Determines which population definitions to show. Use "clustering" after get.graph(), or "celltyping" after manual annotation with celltyping().

Value

A gtable/grob object (from pheatmap) rendered to the graphics device. The heatmap includes hierarchical clustering of both features and populations.

Details

The heatmap content depends on assay type:

• Cytometry: All markers from .tdr.obj$marker

• RNA-seq: Top 3 positive and 3 negative genes per PC (from highly variable genes)

Rows (features) are ordered by hierarchical clustering to group co-expressed markers/genes. Columns (populations) also clustered to reveal relationships between cell types.

The heatmap is computed on-the-fly each time this function is called, using @landmark.annot[[.id.from]]$ids and the expression matrix via .compute_annot_pheatmap(). This ensures the plot always reflects the current active annotations (e.g.\ after set_active_clustering() or set_active_celltyping()).

See also

get.graph, celltyping, set_active_clustering

Examples

if (FALSE) { # \dontrun{
# After clustering
lm.cells <- setup.tdr.obj(.cells = .cells, .meta = .meta) |>
  get.landmarks() |> get.graph()

# Show cluster marker heatmap
plotHeatmap(lm.cells, .id.from = "clustering")

# After manual annotation
lm.cells <- celltyping(lm.cells, 
                       .celltyping.map = list("CD4_T" = c("cluster.1", "cluster.3"),
                                              "CD8_T" = c("cluster.2")))
plotHeatmap(lm.cells, .id.from = "celltyping")
} # }