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plotMetaDataHeatmap

Source: R/plot_heatmap.R
plotMetaDataHeatmap.Rd

Creates heatmap for features within aggregated clusters.

Usage

plotMetaDataHeatmap(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  expression_values = c("normalized", "scaled", "custom"),
  metadata_cols = NULL,
  selected_feats = NULL,
  first_meta_col = NULL,
  second_meta_col = NULL,
  show_values = c("zscores", "original", "zscores_rescaled"),
  custom_cluster_order = NULL,
  clus_cor_method = "pearson",
  clus_cluster_method = "complete",
  custom_feat_order = NULL,
  feat_cor_method = "pearson",
  feat_cluster_method = "complete",
  gradient_color = NULL,
  gradient_midpoint = 0,
  gradient_style = c("divergent", "sequential"),
  gradient_limits = NULL,
  x_text_size = 10,
  x_text_angle = 45,
  y_text_size = 10,
  strip_text_size = 8,
  title = NULL,
  plot_title = deprecated(),
  show_plot = NULL,
  return_plot = NULL,
  save_plot = NULL,
  save_param = list(),
  default_save_name = "plotMetaDataHeatmap"
)

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")

metadata_cols

annotation columns found in pDataDT(gobject)

selected_feats

subset of features to use

first_meta_col

if more than 1 metadata column, select the x-axis factor

second_meta_col

if more than 1 metadata column, select the facetting factor

show_values

which values to show on heatmap (e.g. "zscores", "original", "zscores_rescaled")

custom_cluster_order

custom cluster order (default = NULL)

clus_cor_method

correlation method for clusters, default to "pearson"

clus_cluster_method

hierarchical cluster method for the clusters, default to "complete"

custom_feat_order

custom feature order (default = NULL)

feat_cor_method

correlation method for features, default to "pearson"

feat_cluster_method

hierarchical cluster method for the features, default to "complete"

gradient_color

character. continuous colors to use. palette to use or vector of colors to use (minimum of 2).

gradient_midpoint

numeric. midpoint for color gradient

gradient_style

either 'divergent' (midpoint is used in color scaling) or 'sequential' (scaled based on data range)

gradient_limits

numeric vector with lower and upper limits

x_text_size

size of x-axis text

x_text_angle

angle of x-axis text

y_text_size

size of y-axis text

strip_text_size

size of strip text

title

character. title for plot, defaults to cell_color parameter

plot_title

deprecated. Use title param

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

Value

ggplot or data.table

Details

Creates heatmap for the average expression of selected features in the different annotation/cluster groups. Calculation of cluster or feature order is done on the provided expression values, but visualization is by default on the z-scores. Other options are the original values or z-scores rescaled per feature (-1 to 1).

See also

plotMetaDataCellsHeatmap for numeric cell annotation instead of feature expression.

Examples

g <- GiottoData::loadGiottoMini("visium", verbose = FALSE)
plotMetaDataHeatmap(g, metadata_cols = "leiden_clus",
selected_feats = c("Gna12", "Ccnd2", "Btbd17", "Gm19935"))