plotClusterHeatmap {CATALYST} | R Documentation |
Plots heatmaps summarizing a clustering and/or metaclustering of interest.
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")) )
x |
|
hm2 |
character string. Specifies the right-hand side heatmap. One of:
|
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 |
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. |
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
a HeatmapList-class
object.
Helena Lucia Crowell helena.crowell@uzh.ch
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)
# 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)