plotAbundances: Population frequencies across samples & clusters

Description Usage Arguments Value Author(s) References Examples

View source: R/plotAbundances.R

Description

Plots the relative population abundances of the specified clustering.

Usage

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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
)

Arguments

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.

Value

a ggplot object.

Author(s)

Helena L Crowell helena.crowell@uzh.ch

References

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)

Examples

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# 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"))

CATALYST documentation built on Nov. 8, 2020, 6:53 p.m.