View source: R/plotReducedDim.R
plotReducedDim | R Documentation |
Plot cell-level reduced dimension results stored in a SingleCellExperiment object.
plotReducedDim(
object,
dimred,
ncomponents = 2,
percentVar = NULL,
colour_by = color_by,
shape_by = NULL,
size_by = NULL,
order_by = NULL,
by_exprs_values = "logcounts",
text_by = NULL,
text_size = 5,
text_colour = text_color,
label_format = c("%s %i", " (%i%%)"),
other_fields = list(),
text_color = "black",
color_by = NULL,
swap_rownames = NULL,
point.padding = NA,
force = 1,
rasterise = FALSE,
scattermore = FALSE,
bins = NULL,
summary_fun = "sum",
hex = FALSE,
by.assay.type = by_exprs_values,
...
)
object |
A SingleCellExperiment object. |
dimred |
A string or integer scalar indicating the reduced dimension
result in |
ncomponents |
A numeric scalar indicating the number of dimensions to plot, starting from the first dimension. Alternatively, a numeric vector specifying the dimensions to be plotted. |
percentVar |
A numeric vector giving the proportion of variance in
expression explained by each reduced dimension.
Only expected to be used in PCA settings, e.g., in the
|
colour_by |
Specification of a column metadata field or a feature to
colour by, see the |
shape_by |
Specification of a column metadata field or a feature to
shape by, see the |
size_by |
Specification of a column metadata field or a feature to
size by, see the |
order_by |
Specification of a column metadata field or a feature to
order points by, see the |
by_exprs_values |
Alias for |
text_by |
String specifying the column metadata field with which to add
text labels on the plot.
This must refer to a categorical field, i.e., coercible into a factor.
Alternatively, an AsIs vector or data.frame, see
|
text_size |
Numeric scalar specifying the size of added text. |
text_colour |
String specifying the colour of the added text. |
label_format |
Character vector of length 2 containing format strings
to use for the axis labels.
The first string expects a string containing the result type (e.g.,
|
other_fields |
Additional cell-based fields to include in the
data.frame, see |
text_color |
Alias to |
color_by |
Alias to |
swap_rownames |
Column name of |
point.padding , force |
See |
rasterise |
Whether to rasterise the points in the plot with
|
scattermore |
Logical, whether to use the |
bins |
Number of bins, can be different in x and y, to bin and summarize
the points and their values, to avoid overplotting. If |
summary_fun |
Function to summarize the feature value of each point
(e.g. gene expression of each cell) when the points binned, defaults to
|
hex |
Logical, whether to use |
by.assay.type |
A string or integer scalar specifying which assay to
obtain expression values from,
for use in point aesthetics - see the |
... |
Additional arguments for visualization, see
|
If ncomponents
is a scalar equal to 2, a scatterplot of the first two
dimensions is produced.
If ncomponents
is greater than 2, a pairs plots for the top
dimensions is produced.
Alternatively, if ncomponents
is a vector of length 2, a scatterplot
of the two specified dimensions is produced.
If it is of length greater than 2, a pairs plot is produced containing all
pairwise plots between the specified dimensions.
The text_by
option will add factor levels as labels onto the plot,
placed at the median coordinate across all points in that level.
This is useful for annotating position-related metadata (e.g., clusters)
when there are too many levels to distinguish by colour.
It is only available for scatterplots.
A ggplot object
Arguments shape_by
and size_by
are ignored when
scattermore = TRUE
. Using scattermore
is only recommended for
very large datasets to speed up plotting. Small point size is also
recommended. For larger point size, the point shape may be distorted. Also,
when scattermore = TRUE
, the point_size
argument works
differently.
Davis McCarthy, with modifications by Aaron Lun
example_sce <- mockSCE()
example_sce <- logNormCounts(example_sce)
example_sce <- runPCA(example_sce, ncomponents=5)
plotReducedDim(example_sce, "PCA")
plotReducedDim(example_sce, "PCA", colour_by="Cell_Cycle")
plotReducedDim(example_sce, "PCA", colour_by="Gene_0001")
plotReducedDim(example_sce, "PCA", ncomponents=5)
plotReducedDim(example_sce, "PCA", ncomponents=5, colour_by="Cell_Cycle",
shape_by="Treatment")
# Use scattermore
plotPCA(example_sce, ncomponents = 4, scattermore = TRUE, point_size = 3)
# Bin to show point density
plotPCA(example_sce, bins = 10)
# Bin to summarize values (default is sum)
plotPCA(example_sce, bins = 10, colour_by = "Gene_0001")
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