plotAbundances {CATALYST} | R Documentation |
Plots the relative population abundances of the specified clustering.
plotAbundances( x, k = "meta20", by = c("sample_id", "cluster_id"), group_by = "condition", shape_by = NULL, col_clust = TRUE, distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"), linkage = c("average", "ward.D", "single", "complete", "mcquitty", "median", "centroid", "ward.D2"), k_pal = CATALYST:::.cluster_cols )
x |
|
k |
character string specifying which clustering to use;
valid values are |
by |
a character string specifying whether to plot frequencies by samples or clusters. |
group_by |
character string specifying a non-numeric
cell metadata columnd to group by (determines the color coding);
valid values are |
shape_by |
character string specifying a non-numeric
cell metadata columnd to shape by; valid values are
|
col_clust |
for |
distance |
character string specifying the distance metric
to use for sample clustering; passed to |
linkage |
character string specifying the agglomeration method
to use for sample clustering; passed to |
k_pal |
character string specifying the cluster
color palette; ignored when |
a ggplot
object.
Helena L Crowell helena.crowell@uzh.ch
Nowicka M, Krieg C, Crowell HL, 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) # plot relative population abundances # by sample & cluster, respectively plotAbundances(sce, k = "meta12") plotAbundances(sce, k = "meta8", by = "cluster_id") # use custom cluster color palette plotAbundances(sce, k = "meta10", k_pal = c("lightgrey", "cornflowerblue", "navy"))