Creates heatmap for features and clusters.
Usage
plotHeatmap(
gobject,
spat_unit = NULL,
feat_type = NULL,
expression_values = c("normalized", "scaled", "custom"),
feats,
cluster_column = NULL,
cluster_order = c("size", "correlation", "custom"),
cluster_custom_order = NULL,
cluster_color_code = NULL,
cluster_cor_method = "pearson",
cluster_hclust_method = "ward.D",
feat_order = c("correlation", "custom"),
feat_custom_order = NULL,
feat_cor_method = "pearson",
feat_hclust_method = "complete",
show_values = c("rescaled", "z-scaled", "original"),
size_vertical_lines = 1.1,
gradient_colors = deprecated(),
gradient_color = NULL,
gradient_style = c("divergent", "sequential"),
feat_label_selection = NULL,
axis_text_y_size = NULL,
legend_nrows = 1,
show_plot = NULL,
return_plot = NULL,
save_plot = NULL,
save_param = list(),
default_save_name = "plotHeatmap"
)Arguments
- gobject
giotto object
- spat_unit
spatial unit (e.g. "cell")
- feat_type
feature type (e.g. "rna", "dna", "protein")
- expression_values
expression values to use (e.g. "normalized", "scaled", "custom")
- feats
features to use
- cluster_column
name of column to use for clusters (e.g. "leiden_clus")
- cluster_order
method to determine cluster order (e.g. "size", "correlation", "custom")
- cluster_custom_order
custom order for clusters
- cluster_color_code
color code for clusters
- cluster_cor_method
method for cluster correlation, default to "pearson"
- cluster_hclust_method
method for hierarchical clustering of clusters, default to "ward.D"
- feat_order
method to determine features order (e.g. "correlation", "custom")
- feat_custom_order
custom order for features
- feat_cor_method
method for features correlation, default to "pearson"
- feat_hclust_method
method for hierarchical clustering of features, default to "complete"
- show_values
which values to show on heatmap (e.g. "rescaled", "z-scaled", "original")
- size_vertical_lines
sizes for vertical lines
- gradient_colors
deprecated
- gradient_color
character. continuous colors to use. palette to use or vector of colors to use (minimum of 2).
- gradient_style
either 'divergent' (midpoint is used in color scaling) or 'sequential' (scaled based on data range)
- feat_label_selection
subset of features to show on y-axis
- axis_text_y_size
size for y-axis text
- legend_nrows
number of rows for the cluster legend
- show_plot
logical. show plot
- return_plot
logical. return ggplot object
- save_plot
logical. save the plot
- save_param
list of saving parameters, see
showSaveParameters- default_save_name
default save name for saving, don't change, change save_name in save_param
Details
If you want to display many features there are 2 ways to proceed:
1. set axis_text_y_size to a very small value and show all features
2. provide a subset of features to display to feat_label_selection
Examples
g <- GiottoData::loadGiottoMini("visium", verbose = FALSE)
#>
#> 1. use installGiottoEnvironment() to install
#> a local miniconda python environment along with required modules
#>
#> 2. provide an existing python path to
#> python_path to use your own python path which has all modules
#> installed
#> Set options("giotto.use_conda" = FALSE) if
#> python functionalities are not needed
plotHeatmap(g, feats = c("Gm19935", "Gna12", "Ccnd2", "Btbd17"),
cluster_column = "leiden_clus")
#> Warning: Multiple components found; returning the first one. To return all, use `return_all = TRUE`.
