plotMarkerHeatmap {celda} | R Documentation

## Generate heatmap for a marker decision tree

### Description

Creates heatmap for a specified branch point in a marker tree.

### Usage

    plotMarkerHeatmap(
      tree,
      counts,
      branchPoint,
      featureLabels,
      topFeatures = 10,
      silent = FALSE
    )

### Arguments

`tree` | A decision tree returned from findMarkersTree function.
`counts` | Numeric matrix. Gene-by-cell counts matrix.
`branchPoint` | Character. Name of branch point to plot heatmap for. Name should match those in tree$branchPoints.
`featureLabels` | List of feature cluster assignments. Length should be equal to number of rows in counts matrix, and formatting should match that used in findMarkersTree(). Required when using clusters of features and not previously provided to findMarkersTree()
`topFeatures` | Integer. Number of genes to plot per marker module. Genes are sorted based on their AUC for their respective cluster. Default is 10.
`silent` | Logical. Whether to avoid plotting heatmap to screen. Default is FALSE.

### Value

A heatmap visualizing the counts matrix for the cells and genes at the specified branch point.

### Examples

    ## Not run:
    # Generate simulated single-cell dataset using celda
    sim_counts <- simulateCells("celda_CG", K = 4, L = 10, G = 100)

    # Celda clustering into 5 clusters & 10 modules
    cm <- celda_CG(sim_counts, K = 5, L = 10, verbose = FALSE)

    # Get features matrix and cluster assignments
    factorized <- factorizeMatrix(cm)
    features <- factorized$proportions$cell
    class <- celdaClusters(cm)

    # Generate Decision Tree
    DecTree <- findMarkersTree(features, class, threshold = 1)

    # Plot example heatmap
    plotMarkerHeatmap(DecTree, assay(sim_counts),
      branchPoint = "top_level",
      featureLabels = paste0("L", celdaModules(cm)))

    ## End(Not run)

[Package celda version 1.6.1 Index]