plotAbundances: Population frequencies across samples & clusters
In CATALYST: Cytometry dATa anALYSis Tools

Description Usage Arguments Value Author(s) References Examples

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`	a `SingleCellExperiment`.
`k`	character string specifying which clustering to use; valid values are `names(cluster_codes(x))`.
`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 `names(colData(x))` other than "sample_id" and "cluster_id".
`shape_by`	character string specifying a non-numeric cell metadata columnd to shape by; valid values are `names(colData(x))` other than "sample_id" and "cluster_id".
`col_clust`	for `by = "sample_id"`, specifies whether to hierarchically cluster samples and reorder them accordingly. When `col_clust = FALSE`, samples are ordered according to `levels(x$sample_id)` (or alphabetically, when `x$sample_id` is not a factor).
`distance`	character string specifying the distance metric to use for sample clustering; passed to `dist`
`linkage`	character string specifying the agglomeration method to use for sample clustering; passed to `hclust`.
`k_pal`	character string specifying the cluster color palette; ignored when `by = "cluster_id"`. If less than `nlevels(cluster_ids(x, k))` are supplied, colors will be interpolated via `colorRampPalette`.

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"))