`plotHeatmap`¶

plotHeatmap

Description¶

Creates heatmap for genes and clusters.

Usage¶

plotHeatmap(
  gobject,
  expression_values = c("normalized", "scaled", "custom"),
  genes,
  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",
  gene_order = c("correlation", "custom"),
  gene_custom_order = NULL,
  gene_cor_method = "pearson",
  gene_hclust_method = "complete",
  show_values = c("rescaled", "z-scaled", "original"),
  size_vertical_lines = 1.1,
  gradient_colors = c("blue", "yellow", "red"),
  gene_label_selection = NULL,
  axis_text_y_size = NULL,
  legend_nrows = 1,
  show_plot = NA,
  return_plot = NA,
  save_plot = NA,
  save_param = list(),
  default_save_name = "plotHeatmap"
)

Arguments¶

Argument	Description
`gobject`	giotto object
`expression_values`	expression values to use
`genes`	genes to use
`cluster_column`	name of column to use for clusters
`cluster_order`	method to determine cluster order
`cluster_custom_order`	custom order for clusters
`cluster_color_code`	color code for clusters
`cluster_cor_method`	method for cluster correlation
`cluster_hclust_method`	method for hierarchical clustering of clusters
`gene_order`	method to determine gene order
`gene_custom_order`	custom order for genes
`gene_cor_method`	method for gene correlation
`gene_hclust_method`	method for hierarchical clustering of genes
`show_values`	which values to show on heatmap
`size_vertical_lines`	sizes for vertical lines
`gradient_colors`	colors for heatmap gradient
`gene_label_selection`	subset of genes 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`	show plot
`return_plot`	return ggplot object
`save_plot`	directly save the plot [boolean]
`save_param`	list of saving parameters, see ``showSaveParameters` <#showsaveparameters>`_
`default_save_name`	default save name

Details¶

If you want to display many genes there are 2 ways to proceed:

1. set axis_text_y_size to a really small value and show all genes
1. provide a subset of genes to display to gene_label_selection

Value¶

ggplot

Examples¶

data(mini_giotto_single_cell)

# get all genes
all_genes = slot(mini_giotto_single_cell, 'gene_ID')

# plot heatmap
plotHeatmap(mini_giotto_single_cell,
genes = all_genes[1:10])

# look at cell metadata
cell_metadata = pDataDT(mini_giotto_single_cell)

# plot heatmap per cell type, a column name from cell_metadata
plotHeatmap(mini_giotto_single_cell,
genes = all_genes[1:10],
cluster_column = 'cell_types')