`plotMetaDataHeatmap`¶

plotMetaDataHeatmap

Description¶

Creates heatmap for genes within aggregated clusters.

Usage¶

plotMetaDataHeatmap(
  gobject,
  expression_values = c("normalized", "scaled", "custom"),
  metadata_cols = NULL,
  selected_genes = 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_gene_order = NULL,
  gene_cor_method = "pearson",
  gene_cluster_method = "complete",
  gradient_color = c("blue", "white", "red"),
  gradient_midpoint = 0,
  gradient_limits = NULL,
  x_text_size = 10,
  x_text_angle = 45,
  y_text_size = 10,
  strip_text_size = 8,
  show_plot = NA,
  return_plot = NA,
  save_plot = NA,
  save_param = list(),
  default_save_name = "plotMetaDataHeatmap"
)

Arguments¶

Argument	Description
`gobject`	giotto object
`expression_values`	expression values to use
`metadata_cols`	annotation columns found in pDataDT(gobject)
`selected_genes`	subset of genes 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
`custom_cluster_order`	custom cluster order (default = NULL)
`clus_cor_method`	correlation method for clusters
`clus_cluster_method`	hierarchical cluster method for the clusters
`custom_gene_order`	custom gene order (default = NULL)
`gene_cor_method`	correlation method for genes
`gene_cluster_method`	hierarchical cluster method for the genes
`gradient_color`	vector with 3 colors for numeric data
`gradient_midpoint`	midpoint for color gradient
`gradient_limits`	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
`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¶

Creates heatmap for the average expression of selected genes in the different annotation/cluster groups.: Calculation of cluster or gene 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 gene (-1 to 1).

Value¶

ggplot or data.table

Seealso¶

``plotMetaDataCellsHeatmap` <#plotmetadatacellsheatmap>`_ for numeric cell annotation instead of gene expression.

Examples¶

data(mini_giotto_single_cell)

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

# look at cell metadata
cell_metadata = pDataDT(mini_giotto_single_cell)

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