Description Usage Arguments Value Author(s) References Examples
View source: R/plotAbundances.R
Plots the relative population abundances of the specified clustering.
1 2 3 4 5 6 7 8 9 10 11 12 | 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 |
a |
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)
1 2 3 4 5 6 7 8 9 10 11 12 13 | # 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"))
|
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