plotClusterHeatmap {CATALYST} | R Documentation

## Plot cluster heatmap

### Description

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

### Usage

    plotClusterHeatmap(x, ...)

    ## S4 method for signature 'daFrame'
    plotClusterHeatmap(x, hm2 = NULL, k = 20, m = NULL,
      cluster_anno = TRUE, split_by = NULL, scale = TRUE, draw_dend = TRUE,
      draw_freqs = FALSE, palette = rev(brewer.pal(11, "RdYlBu")))

### Arguments

`x` | a `daFrame`.
`...` | optional arguments.
`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` | numeric or character string. Specifies the clustering across which median marker expressions should be computed.
`m` | numeric or character string. Specifies the metaclustering to be shown. (This is for display only and will not effect any computations!)
`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 crowellh@student.ethz.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

    data(PBMC_fs, PBMC_panel, PBMC_md)
    re <- daFrame(PBMC_fs, PBMC_panel, PBMC_md)

    # run clustering
    lineage <- c("CD3", "CD45", "CD4", "CD20", "CD33",
        "CD123", "CD14", "IgM", "HLA_DR", "CD7")
    re <- cluster(re, cols_to_use=lineage)

    plotClusterHeatmap(re, hm2="abundances")
    plotClusterHeatmap(re, hm2="state_markers", k=16, split_by='condition')
    plotClusterHeatmap(re, hm2="pS6", k=12, m=8)

[Package CATALYST version 1.4.2 Index]