vizAPOTC | R Documentation |
This function combines the functionality of both RunAPOTC()
and
APOTCPlot()
. Given a Seurat object, it first runs the APackOfTheClones
method (RunAPOTC()
) to compute clonal expansion information, and then
generates a ggplot2 object of the clonal expansion plot with
a circle size legend. (APOTCPlot()
)
vizAPOTC(
seurat_obj,
reduction_base = "umap",
clonecall = "strict",
...,
extra_filter = NULL,
alt_ident = NULL,
clone_scale_factor = "auto",
rad_scale_factor = 0.95,
order_clones = TRUE,
try_place = FALSE,
repulse = TRUE,
repulsion_threshold = 1,
repulsion_strength = 1,
max_repulsion_iter = 20L,
show_shared = NULL,
only_link = NULL,
show_all_links = FALSE,
clone_link_width = "auto",
clone_link_color = "black",
clone_link_alpha = 0.5,
res = 360L,
linetype = "blank",
use_default_theme = TRUE,
retain_axis_scales = FALSE,
alpha = 1,
show_labels = FALSE,
label_size = 5,
add_size_legend = TRUE,
legend_sizes = "auto",
legend_position = "auto",
legend_buffer = 0.2,
legend_color = "#808080",
legend_spacing = "auto",
legend_label = "Clone sizes",
legend_text_size = 5,
add_legend_background = TRUE,
add_legend_centerspace = 0,
detail = TRUE,
verbose = TRUE
)
Note that the subsetting arguments ...
and extra_filter
are only a
quick convenience to subset based on metadata, and the subset
S3 method
defined in Seurat
is much more mature are has more features. Additionally,
users need to work with data subsets are recommended to and likely already
are working with seurat objects subsetted/split with Seurat::SplitObject
.
A ggplot object of the APackOfTheClones clonal expansion plot of the
seurat object. There is an additional 10th element in the object named
"APackOfTheClones"
used by other functions in this package and shouldn't
interfere with any other ggplot functionality. (As far as currently known)
For the ident that was used to cluster the clones, labels for each cluster
are inferred and stored in the run so that they can be used by other
functions and optionally overlaid on the plot over clusters. If the levels
of the ident used is a naturally ordered integer sequence, then the labels
generated would be "C1", "C2", "C3" ...
, else they would be the actual
ident levels themselves.
AdjustAPOTC()
data("combined_pbmc")
# plot with default parameters
vizAPOTC(combined_pbmc, verbose = FALSE)
# use arguments from RunAPOTC and APOTCPlot
vizAPOTC(
combined_pbmc, try_place = TRUE, show_labels = TRUE, verbose = FALSE
)
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