plotPbExprs {CATALYST}

R Documentation

Pseudobulk-level boxplot

Description

Boxplot of aggregated marker data by sample or cluster, optionally colored and faceted by non-numeric cell metadata variables of interest.

Usage

plotPbExprs(
  x,
  k = "meta20",
  features = "state",
  assay = "exprs",
  fun = c("median", "mean", "sum"),
  facet_by = c("antigen", "cluster_id"),
  color_by = "condition",
  group_by = color_by,
  shape_by = NULL,
  size_by = FALSE,
  geom = c("both", "points", "boxes"),
  jitter = TRUE,
  ncol = NULL
)

plotMedExprs(
  x,
  k = "meta20",
  features = "state",
  facet_by = c("antigen", "cluster_id"),
  group_by = "condition",
  shape_by = NULL
)

Arguments

x	a SingleCellExperiment{SingleCellExperiment}.
k	character string specifying which clustering to use; values values are names(cluster_codes(x)). Ignored if facet_by = "antigen".
features	character vector specifying which features to include; valid values are "type"/"state" for type/state_markers(x) if rowData(x)$marker_class have been specified; a subset of rownames(x); NULL to use all features.
assay	character string specifying which assay data to use; valid values are assayNames(x).
fun	character string specifying the summary statistic to use.
facet_by	"antigen" or "cluster_id"; the latter requires having run cluster.
color_by, group_by, shape_by	character string specifying a non-numeric cell metadata variable to color, group and shape by, respectively; valid values are names(colData(x)) and names(cluster_codes(x)) if cluster has been run.
size_by	logical specifying whether to scale point sizes by the number of cells in a given sample or cluster-sample instance; ignored when geom = "boxes".
geom	character string specifying whether to include only points, boxplots or both.
jitter	logical specifying whether to use position_jitterdodge in geom_point when geom != "boxes".
ncol	integer scalar specifying number of facet columns.

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

# construct SCE
data(PBMC_fs, PBMC_panel, PBMC_md)
sce <- prepData(PBMC_fs, PBMC_panel, PBMC_md)
sce <- cluster(sce, verbose = FALSE)

# plot median expressions by sample & condition
# ...split by marker
plotPbExprs(sce, 
  shape_by = "patient_id",
  features = sample(rownames(sce), 6))

# ...split by cluster
plotPbExprs(sce, facet_by = "cluster_id", k = "meta6")

# plot median type-marker expressions by sample & cluster
plotPbExprs(sce, feature = "type", k = "meta6", 
  facet_by = "antigen", group_by = "cluster_id", color_by = "sample_id",
  size_by = TRUE, geom = "points", jitter = FALSE, ncol = 5)
  
# plot median state-marker expressions 
# by sample & cluster, split by condition
plotPbExprs(sce, k = "meta6", facet_by = "antigen", 
  group_by = "cluster_id", color_by = "condition", ncol = 7)

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