pbMDS | R Documentation |
Pseudobulk-level Multi-Dimensional Scaling (MDS) plot computed on median marker expressions in each sample.
pbMDS(
x,
by = c("sample_id", "cluster_id", "both"),
k = "meta20",
dims = c(1, 2),
features = NULL,
assay = "exprs",
fun = c("median", "mean", "sum"),
color_by = switch(by, sample_id = "condition", "cluster_id"),
label_by = if (by == "sample_id") "sample_id" else NULL,
shape_by = NULL,
size_by = is.null(shape_by),
pal = if (color_by == "cluster_id") .cluster_cols else NULL
)
x |
a |
by |
character string specifying whether to aggregate
by |
k |
character string specifying which clustering to use when
|
dims |
two numeric scalars indicating which dimensions to plot. |
features |
character string specifying which features to include
for computation of reduced dimensions; valid values are
|
assay |
character string specifying which assay data to use;
valid values are |
fun |
character string specifying which summary statistic to use. |
color_by |
character string specifying a
non-numeric cell metadata column to color by;
valid values are |
label_by |
character string specifying a
non-numeric cell metadata column to label by;
valid values are |
shape_by |
character string specifying a
non-numeric cell metadata column to shape by;
valid values are |
size_by |
logical specifying whether points should be sized by the number of cells that went into aggregation; i.e., the size of a give sample, cluster or cluster-sample instance. |
pal |
character vector of colors to use;
NULL for default |
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)
data(PBMC_fs, PBMC_panel, PBMC_md)
sce <- prepData(PBMC_fs, PBMC_panel, PBMC_md)
sce <- cluster(sce)
# sample-level pseudobulks
# including state-markers only
pbMDS(sce, by = "sample_id", features = "state")
# cluster-level pseudobulks
# including type-features only
pbMDS(sce, by = "cluster_id", features = "type")
# pseudobulks by cluster-sample
# including all features
pbMDS(sce, by = "both", k = "meta12",
shape_by = "condition", size_by = TRUE)
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