GetAssayData and SetAssayData
functions.YlGnBu.feature_to_rank if
continous_feature = TRUE.sequential.direction to
1.legend.position to
bottom when continuous_feature = FALSE.legend.position to
bottom.use_silhouette = TRUE.min.cutoff or
max.cutoff would render the values outside these bounds to
NA and therefore being plotted as grey. Now they will have the
highest/lowest value possible.symmetry.type parameter, that allows to control
how the symmetry is computed: either in absolute values (taking into
account the highest and lowest value) or in the middle point specified
by symmetry.center.symmetry.center parameter, that allows to control
the center of symmetry when symmetry.type is set to
centered.legend.position to
bottom.plot_boxplots = FALSE.legend.title.position parameter and set it up as
top by default.SCpubr::check_dependencies() to support the use
of SCpubr::package_report().SCpubr::do_EnrichmentHeatmapThis major update focus on a complete re-implementation of all
heatmap-based functions into ggplot2 instead of
ComplexHeatmap. This will lead to many of the existing code
to break. The trade-off between the difficulty of debug, expand and
maintain the existing heatmap-based functions with regards to the
capabilities ComplexHeatmap offers with regards to ggplot2 was not
worthy.
All heatmap-specific parameters have been replaced with the overarching parameters that are used across functions. This decision was taking after a lot of thought, but ultimately, having all plots rely on ggplot2 makes it way more compatible to work with them together, to debug, and to further implement new ideas.
Many (except a few selected cases) of the functions that returned list of different plots have been modified to return a single (and most important/relevant) plot and the option to return the Seurat object with the data generated added to it has been implemented so that the user can still generate plots with it. This goes in line with the fact that having so many interconnected functions made it very difficult to expand on them, if needed, as the downstream effects will cascade to other functions as well.
viridis_color_map to
viridis.palette.viridis_direction to
viridis.direction.sequential_direction to
sequential.direction.rotate_x_axis_labels to
axis.text.x.angle.rotate_strip_text to
strip.text.angle.SCpubr::do_MetadataPlot() to generate metadata heatmaps
with ease both from Seurat object or from a data frame. Will be first
released as part of the development version and then
released in CRAN as part of future updates. The idea is to gather
feedback from users before officially releasing it.SCpubr::do_SCExpressionHeatmap() to generate heatmaps
of expression of genes across all cells in the dataset. Will be first
released as part of the development version and then
released in CRAN as part of future updates. The idea is to gather
feedback from users before officially releasing it.SCpubr::do_SCEnrichementHeatmap() to generate heatmaps
of enrichment of genes across all cells in the dataset. Will be first
released as part of the development version and then
released in CRAN as part of future updates. The idea is to gather
feedback from users before officially releasing it.SCpubr::do_AffinityAnalysisPlot() to assess the
affinity of gene sets to subset of cells in the Seurat objects using the
weighted means algorithms from DecoupleR. Will be first released as part
of the development version and then released in CRAN as
part of future updates. The idea is to gather feedback from users before
officially releasing it.SCpubr::do_LoadingsPlot() to generate a summary heatmap
of the PCA loadings (top and bottom scored genes for each PC) together
with a expression heatmap of the same genes. Will be first released as
part of the development version and then released in CRAN
as part of future updates. The idea is to gather feedback from users
before officially releasing it.SCpubr::do_DiffusionMapPlot() to analyze the output of
a diffusion map analysis on the context of enrichment in gene sets used
for the generation of the diffusion map. Will be first released as part
of the development version and then released in CRAN as
part of future updates. The idea is to gather feedback from users before
officially releasing it.SCpubr::check_dependencies() to generate a per-function
summary of the needed packages to run the function. The report has been
enhanced with cli package and now clearly illustrates what
is missing to run the function.SCpubr::do_SankeyPlot() has been removed and replaced
by SCpubr::do_AlluvialPlot(), which is present in the
official CRAN versions.SCpubr::do_PseudotimePlot() has been removed
indefinitely until a better, revamped, state-of-the-art version is
generated.SCpubr::do_AzimuthAnalysisPlot() has been removed as
the output can be accomplished by a combination of the current functions
in SCpubr. A vignette will be added to reproduce the same
analysis.min.cutoff or max.cutoff,
the legend will show that the min/max value is higher/lower than the one
provided, if such value appeared originally in the legend breaks. This
potentially interacts with enforce_symmetry.number.breaks parameter to control the number of
breaks in the legend of ggplot2-based plots. It will not always work, as
the function will try to fit the breaks accordingly. But still, will
give some range of freedom to the user.colorsteps from legend.type
parameters as it was prone to generate unintended bugs.min.cutoff and
max.cutoff from NULL to NA.diverging.palette parameter in all plots
that have a symmetrical color scale to help selecting other possible
color scales for the plot.sequential.palette parameter in all plots
that have a continuous, non-symmetrical color scale to help selecting
other possible color scales for the plot, in the case the user does not
want to use viridis color scales.SCpubr::state_dependencies() to
SCpubr::check_dependencies().cli package.plot.title.face: To control the style of the
title.plot.subtitle.face: To control the style of the
subtitle.plot.caption.face: To control the style of the
caption.axis.title.face: To control the style of the
axis title.axis.text.face: To control the style of the
axis text.legend.title.face: To control the style of the
legend title.legend.text.face: To control the style of the
legend text.bold to
plain.bold to
plain.plot.axes = TRUE parameter in
SCpubr::do_DimPlot(), SCpubr::do_FeaturePlot()
and SCpubr::do_NebulosaPlot(), now the entirety of the X
and Y axis is removed, titles included.SCpubr::do_DimPlot(),
SCpubr::do_FeaturePlot() and
SCpubr::do_NebulosaPlot().SCpubr::do_AlluvialPlotsequential.palette and
sequential.direction parameters.SCpubr::do_BarPlotfacet.by parameter to extra group the bars by a
third metadata variable.order.by to reorder the bars when using
position = fill based on a value in
group.by.group.by,
split.by and order.by to those that make sense
to plot. For instance, a bar plot using group.by and
position = fill but not using split.by
resulted in bars of equal lenght with only one value per group of
proportion 1.plot.grid to
FALSE.add.n to display the total count on top
when position = fill.add.n.face to control the appearance of
the text displayed.add.n.expand to control the range of
values in the Y axis. This has to be minimum 0 and maximum at least 1.
This is set in order to tweak the limits so that the labels fit when
flip = TRUE.SCpubr::do_BeeSwarmPlotorder parameter to reorder the groups based on
the median rank.SCpubr::do_BoxPlotna.rm to geom_boxplot to avoid
unnecessary warnings when introducing NAs as part of the data.order would not work if
NAs are in the data.boxplot.linewidth from
1 to 0.5.group.by and split.by, the package would check
that the colors provided to colors.use need to match the
values in group.by and not split.by.SCpubr::do_CorrelationPlotmin.cutoff and max.cutoff parameter
to add cutoffs to the scales.mode = "jaccard" to compute a correlation matrix
of a list of gene sets based on jaccard similarity.use_viridis, sequential.palette and
sequential_direction and diverging.palette to
control color palettes.cluster parameter to toggle on/off the clustering
of the rows and columns in the heatmap.remove.diagonal parameter to toggle on/off the
conversion of the diagonal in the correlation matrix to
NA.min.cutoff and max.cutoff.number.breaks will not work in
mode = "jaccard".SCpubr::do_CopyNumberVariantPlot()return_object has been added
to return the Seurat object with a new assay containing the CNV scores
per cell on the data slot of the CNV_scores
assay.SCpubr::do_DimPlotcells.highlight or idents.hightlight or
idents.keep are used. Also removed the “Not selected” item
from the legend when doing so, as it was redundant.group.by and split.by, given
that the individual UMAPs would not have the same number of entities to
plot and color.SCpubr::do_DotPlotscale parameter to allow for the data to be
scaled or not scaled.split.by parameter in favor or the higher
consistency and proper functionality accross parameters. Will probably
come in the future, implemented outside of the umbrella of Seurat.cluster.idents to
cluster.flip when
features was a list of genes. Now any combination of
flip and features is possible.SCpubr::do_EnrichmentHeatmapreturn_object = TRUE and plot the enrichment scores
separately, that are stored as a new Assay.return_matrix parameter as the scores can now
be retrieved from the Seurat object as an assay.named lists as input for the
function.cluster parameter to allow for clustering of rows
and columns.groups.order to allow for specifically arrange
the groups defined by group.by with a given order.features.order to allow for specifically arrange
the gene sets defined by input_gene_list.SCpubr::do_ExpressionHeatmapcluster parameter to allow for clustering of rows
and columns.groups.order to allow for specifically arrange
the groups defined by group.by with a given order.features.order to allow for specifically arrange
the features defined by features.SCpubr::do_FeaturePlotsplit.by, cells.hightlight and
idents.highlight.border.density to reduce the amount of
extra cells drawn on the background to generate the borders. This will
be a number between 0 and 1 corresponding to the quantile of the
distribution of density of the points in the scatterplot drawn in the
background. The lower the value, the harder it will be to keep a border
around all cells, while it will significantly reduce the overall weight
of the plot object.group.by, that allows to plot a big dot
in the center of each group designated by group.by and thus
allowing to locate easily where each identity is in the FeaturePlot.
Also, plots a legend matching the color of the dots. This can be tweaked
with additional parameters such as:group.by.show.dots to controlw hether these dots are
plotted or not (to allow only plotting colored borders around cells -
see below).group.by.dot.size to control the size of the introduced
dots.group.by.cell_border to plot another contour-like
border which also displays the color coding of the clusters designated
by group.by, to signal the reach of each cluster. However,
this basically signals the cluster the cells in the periphery of the
cell blobs belong to. Take that into account.group.by.cell_borders.alpha controls the alpha of the
new cell borders.group.by.legend controls the legend title of the new
legend.split.by.idents to idents.keep to
better synergize with the parameter in SCpubr::do_DimPlot.
Only works when split.by is used.SCpubr::do_FunctionalAnnotationPlotreturn_matrix is added, so that the user can use it to
compute further analysis or visualizations.SCpubr::do_FunctionalAnnotationPlotorder_by_mean to order.order = TRUE now is done based on the
median instead of the mean.SCpubr::do_LigandReceptorPlot()liana::liana_aggregate() is taken into account.arrange_interactions_by as now the function
only accepts the output of liana::liana_aggregate().sort.by parameter instead to select how the
output of liana::liana_aggregate() should be ordered prior
the subset by top_interactions. Five modes are available:
A: Orders the output by specificity.B: Orders the output by magnitude.C: Orders the output by specificity then
magnitude. This prioritizes the specificity
column.D: Orders the output by magnitude then
specificity. This prioritizes the magnitude
column.E: Orders the output by specificity and
magnitude providing equal weights to both columns.flip parameter as the output was prone to
errors.compute_ChordDiagrams and added
return_interactions. This parameter returns two tibbles
that can be used alongside SCpubr::do_ChordDiagramPlot to
plot the diagrams.keep_source and
keep_target takes place before subsetting for the top N
interactions defined by top_interactions. This ensures
that, if the user wants to focus on a given interaction subset, we
retrieve the most important interactions for the subset.magnitude and specificity columns to
allow the user to choose which variables to use for plotting.sorting.type.magnitude and
sorting.type.specificity to allow the user to choose how
the columns are sorted prior plotting.invert_magnitude and
invert_specificity to allow the user to choose how the data
is displayed for columns that tend to 0. Inverting performs a
-log10 transformation on the column.verbose parameter and set it to
TRUE by default to inform the user of the arrangements
taking place in the output of liana::liana_aggregate()
prior plotting.SCpubr::do_PathwayActivityPlot()return_object parameter that returns the
Seurat object with the new assay to use for other plotting purposes
(such as Geyser and Feature plots).return_object = TRUE and plot the scores separately.slot parameter to decide whether to plot scale
data or not.enforce_symmetry = FALSE the color palette used was
diverging.palette instead.SCpubr::do_TFActivityPlot()return_object parameter that returns the
Seurat object with the new assay to use for other plotting purposes
(such as Geyser and Feature plots).return_object = TRUE and plot the scores separately.slot parameter to decide whether to plot scale
data or not.enforce_symmetry = FALSE the color palette used was
diverging.palette instead.More hotfixes in unit tests to comply with CRAN checks.
Hotfixes in unit tests to comply with CRAN checks.
element_line,do_AlluvialPlot(),
do_AzimuthAnalysisPlot(),
do_ExpressionHeatmap(), do_GroupedGOTermPlot()
and do_FunctionalAnnotationPlot().legend.ncol, legend.nrow,
legend.title and legend.byrow to as many
functions as possible to further customize legend appearance.SCpubr::do_BeeSwarmPlot()min.cutoff and max.cutoff
parameter.viridis_direction parameter to control how the
continuous color scale is formed.return_object parameter to return the Seurat
object with the enrichment scores computed.legend.position conditional of whether
continuous_feature is set to TRUE. If it is false, legend
is not displayed unless the user specifies otherwise.SCpubr::do_BarPlot()flip and
split.by.x_lab and y_lab would
not rotate accordingly when using flip = TRUE.SCpubr::do_BeeSwarmPlot()groupOnX parameter of
geom_quarirandom. This will likely affect users with a
lower version.SCpubr::do_BoxPlot()assay to NULL and will default to the default assay
in the seurat object.SCpubr::do_CellularStatesPlot()SCpubr::do_CorrelationPlot()viridis_direction parameter.SCpubr::do_DimPlot()legend.title was
used.split.by works, so that
now only one legend is displayed for the whole group and cells have
border.label.size and label.box parameters
for further customize the appearance of the plot when using
label = TRUE.repel to FALSE by default.SCpubr::do_EnrichmentHeatmap()flavor = "AUCell", that lets the user compute
AUCell scoring of the gene sets.group.by parameters
at the same time.group.by to be returned properly,
leading to the last value of group.by replacing all the
rest.SCpubr::do_FeaturePlot()label, label.size and
label.color parameter to reproduce the same behavior as in
Seurat::FeaturePlot().SCpubr::do_GroupwiseDEPlot()assay to NULL and will default to the default assay
in the seurat object.SCpubr::do_LigandReceptorPlot()arrange_interactions_by to control how output
interactions are arranged (either by aggregate_rank, specificity,
magnitude or a combination of magnitude and specificity).sort_interactions_alphabetically to control
whether the output dotplot has the interactions ordered alphabetically
or as they come in the original matrix (meaning, they follow the
arrangement specified in arrange_interactions_by). ((liana’s issue
#72))do_PathwayActivityPlot()enforce_symmetry is set to
FALSE, then the color scale turns into a viridis-based one
instead of a two-color gradient scale.do_TFActivityPlot()enforce_symmetry is set to
FALSE, then the color scale turns into a viridis-based one
instead of a two-color gradient scale.SCpubr::do_ViolinPlot()colors.use.do_GeyserPlot with
categorical variables had a bug that mapped the legend to the continuous
axis.min.cutoff and max.cutoff parameter
to effectively subset the color scale and remove the effect of extreme
outliers in all ComplexHeatmap-based functions.min.cutoff and max.cutoff parameter
to effectively subset the color scale and remove the effect of extreme
outliers in all ggplot2-based functions susceptible to be biased by
outliers.SCpubr::do_DimPlot()split.by and
group.by in combination, the cells colored on top of the
UMAP also have a border.split.by and
group.by in combination, the extra new layers would not
raster if raster = TRUE.split.by and
group.by in combination, no plots will appear if
ncol is set.plot_density_contour.raster.dpi to Seurat
versions higher or equal to 4.1.0.SCpubr::do_EnrichmentHeatmap()plot_FeaturePlots and
plot_GeyserPlots to also report the enrichment scores in a
gene set-based manner.flavor parameter, that accepts
Seurat and UCell to allow for different
enrichment scoring methods. It requires R 4.2.0 to
run.symmetrical_scale to
enforce_symmetry to have a greater coherence across
functions.SCpubr::do_FeaturePlot()plot_density_contour.raster.dpi to Seurat
versions higher or equal to 4.1.0.SCpubr::do_GeyserPlot()color.by implementation due to it being very
buggy. This will be re-implemented in a future patch.SCpubr::do_RidgePlot()assay = "RNA" or,
in fact, any other assay rather than SCT will result in an
error.SCpubr::do_ViolinPlot()split.by was an actual line of code.legend.title parameter to control the title of
the legend.SCpubr::save_Plot()
SCpubr::do_LigandReceptorPlot() and
SCpubr::do_SankeyPlot().enforce_symmetry = TRUE
to have the middle color as grey95 instead of the previous
one, which made middle values seem closer to the positive end of the
scale.rotate_x_axis_labels parameter in all
functions that made use of it. Now, instead of accepting a
logical, accepts a numeric: either
0, 45 or 90, corresponding to the
degrees in which the X axis labels should be rotated. (#5)SCpubr::do_CopyNumberVariantPlotSCpubr::do_CopyNumberVariantPlot
to also report results for the whole chromosome as well as for each
chromosome arm.verbose argument to
SCpubr::do_CopyNumberVariantPlot to silence the messages
when there are not enough genes in the chromosome to perform the
analysis.SCpubr::do_DimPlot()SCpubr::do_DimPlot() when cell borders are displayed.group.by and split.by functionality
to SCpubr::do_DimPlot(). (#4)SCpubr::do_DotPlot()SCpubr::do_FeaturePlot()SCpubr::do_FeaturePlot when
setting enforce_symmetry = TRUE.order in
SCpubr::do_FeaturePlot() from TRUE to
FALSE.min.cutoff and max.cutoff parameters
to SCpubr::do_FeaturePlot(). This allows to effectively
subset the color scale to the values provided. Cells outside the range
will be converted to the min or max values provided, respectively. (#2)SCpubr::do_GeyserPlot()flip parameter.SCpubr::do_GroupwiseDEPlot()SCpubr::do_GroupwiseDEPlot in which the
heatmap could not be computed. (#3)SCpubr::do_GroupwiseDEPlot. (#3)SCpubr::do_LigandReceptorPlot()
(development release)x_labels_angle to
rotate_x_axis_labels to keep a consistent terminology.SCpubr::do_RidgePlot()panel.grid.minor to
be displayed in SCpubr::do_Ridgeplot().flip parameter.SCpubr::do_ViolinPlot()split.by functionality to
SCpubr::do_ViolinPlot(). (#4, #5)flip parameter.feature parameter to features, to
better reflect the multiple feature behavior.Seurat’s share.y.lims behavior
and set it to share.y.lims parameter. (#5)SCpubr::save_Plot()
SCpubr::do_LigandReceptorPlot() and
SCpubr::do_SankeyPlot().SCpubr::save_Plot()
SCpubr::do_LigandReceptorPlot() and
SCpubr::do_SankeyPlot().Seurat do not require this to be installed. This is just
for the very side case in which somebody downloads the package just for
the SCpubr::do_ColorPalette() function.individual.titles,
’individual.subtitlesandindividual.captionsinSCpubr::do_NebulosaPlot()`
as the benefit of such parameters did not surpass the problems the code
was causing. The feature might come back in the future, once fully
optimized.SCpubr::save_Plot() function to align with CRAN
policies that the package should not write to the file system. The code
is still available in the v0.0.0.0.9000 release in Github.SCpubr::do_LigandReceptorPlot(),
SCpubr::do_SankeyPlot() and
SCpubr::do_PseudotimePlot() to align with CRAN policies and
make it possible to publish the package. These functions can still be
accessed in the v0.0.0.0.9000 release in Github.SCpubr::do_PseudotimePlot() for the reason
above and because the dependency Matrix.utils was removed
from CRAN on 09-10-2022.SCpubr::save_Plot()
SCpubr::do_LigandReceptorPlot() and
SCpubr::do_SankeyPlot().NEWS.md file to track changes to the
package.