plotClusterHeatmap {CATALYST} | R Documentation |
Plots heatmaps summarizing a clustering and/or metaclustering of interest.
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")))
x |
a |
... |
optional arguments. |
hm2 |
character string. Specifies the right-hand side heatmap. One of:
|
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 |
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 crowellh@student.ethz.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)
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)