plotClusterHeatmap {CATALYST} | R Documentation

## Plot cluster heatmap

### Description

Plots heatmaps summarizing a clustering and/or metaclustering of interest.

### Usage

    plotClusterHeatmap(
      x,
      hm2 = NULL,
      k = "meta20",
      m = NULL,
      fun = c("median", "mean"),
      cluster_anno = TRUE,
      split_by = NULL,
      scale = TRUE,
      draw_dend = TRUE,
      draw_freqs = FALSE,
      palette = rev(brewer.pal(11, "RdYlBu"))
    )

### Arguments

`x` | a `SingleCellExperiment`.
`hm2` | character string. Specifies the right-hand side heatmap. One of: `"abundances"`: cluster frequencies across samples `"state_markers"`: median cell state marker expressions across clusters (analogous to the left-hand side heatmap) a character string/vector corresponding to one/multiple marker(s): median marker expressions across samples and clusters
`k` | character string. Specifies the clustering across which median marker expressions should be computed.
`m` | character string. Specifies the metaclustering to be shown. (This is for display only and will not effect any computations!)
`fun` | character string specifying the function to use as summary statistic.
`cluster_anno` | logical. Specifies if clusters should be annotated.
`split_by` | character string. Must corresponds to a column name of `rowData(x)`. If specified, the data will be subset according to this variable, and multiple heatmaps will be drawn.
`scale` | logical. Specifies whether scaled values should be plotted. (see below for details)
`draw_dend` | logical. Specifies if the row dendrogram should be drawn.
`draw_freqs` | logical. Specifyies whether to display cell counts and proportions.
`palette` | character vector of colors to interpolate.

### Details

Scaled values corresponds to cofactor arcsinh-transformed expression values scaled between 0 and 1 using 1 boundaries. Hierarchical clustering is performed on the unscaled data.

In its 1st panel, `plotClusterHeatmap` will display median (scaled, arcsinh-transformed) cell-type marker expressions (across all samples). Depending on argument `hm2`, the 2nd panel will contain one of:

- relataive cluster abundances by sample

- median (scaled, arcsinh-transformed) cell-state marker expressions (across all samples)

- median (scaled, arcsinh-transformed) cell-state marker expressions by sample

### Value

a `HeatmapList-class` object.

### Author(s)

Helena Lucia Crowell helena.crowell@uzh.ch

### References

Nowicka M, Krieg C, Weber LM et al. CyTOF workflow: Differential discovery in high-throughput high-dimensional cytometry datasets. F1000Research 2017, 6:748 (doi: 10.12688/f1000research.11622.1)

### Examples

    # construct SCE & run clustering
    data(PBMC_fs, PBMC_panel, PBMC_md)
    sce <- prepData(PBMC_fs, PBMC_panel, PBMC_md)
    sce <- cluster(sce)

    plotClusterHeatmap(sce, hm2="abundances")
    plotClusterHeatmap(sce, hm2="abundances", draw_freqs=TRUE)
    plotClusterHeatmap(sce, hm2="state_markers", k="meta16", split_by='condition')
    plotClusterHeatmap(sce, hm2="pS6", k="meta12", m="meta8")
    plotClusterHeatmap(sce, hm2="abundances", scale=FALSE, draw_freqs=TRUE)

[Package CATALYST version 1.10.3 Index]