plotPbExprs {CATALYST} | R Documentation |
Boxplot of aggregated marker data by sample or cluster, optionally colored and faceted by non-numeric cell metadata variables of interest.
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 )
x |
a |
k |
character string specifying which clustering to use;
values values are |
features |
character vector specifying
which features to include; valid values are
|
assay |
character string specifying which assay data
to use; valid values are |
fun |
character string specifying the summary statistic to use. |
facet_by |
|
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
|
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 |
character string specifying whether to include only points, boxplots or both. |
jitter |
logical specifying whether to use |
ncol |
integer scalar specifying number of facet columns. |
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 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)