iheatmap: iheatmap

In iheatmapr: Interactive, Complex Heatmaps

iheatmap

Description

Make a farily standard interactive heatmap with optional clustering and row and column annotations. For more flexibility and options, see the main_heatmap function and other modular functions as described in vignette.

Usage

## S4 method for signature 'matrix'
iheatmap(
  data,
  x = default_x(data),
  y = default_y(data),
  cluster_rows = c("none", "hclust", "kmeans"),
  cluster_cols = c("none", "hclust", "kmeans"),
  row_clusters = NULL,
  col_clusters = NULL,
  row_k = NULL,
  col_k = NULL,
  row_clust_dist = stats::dist,
  col_clust_dist = stats::dist,
  name = "Signal",
  scale = c("none", "rows", "cols"),
  scale_method = c("standardize", "center", "normalize"),
  colors = NULL,
  col_clusters_colors = NULL,
  col_clusters_name = "Col<br>Clusters",
  row_clusters_colors = NULL,
  row_clusters_name = "Row<br>Clusters",
  show_row_clusters_colorbar = TRUE,
  show_col_clusters_colorbar = TRUE,
  row_annotation = NULL,
  col_annotation = NULL,
  row_annotation_colors = NULL,
  col_annotation_colors = NULL,
  row_labels = NULL,
  col_labels = NULL,
  row_title = NULL,
  col_title = NULL,
  colorbar_grid = setup_colorbar_grid(),
  layout = list(),
  source = "iheatmapr",
  ...
)

Arguments

data	matrix of values to be plotted as heatmap
x	x xaxis labels, by default colnames of data
y	y axis labels, by default rownames of data
cluster_rows	"none","hclust", or "k-means" for no clustering, hierarchical clustering, and k-means clustering of rows respectively
cluster_cols	"none","hclust", or "k-means" for no clustering, hierarchical clustering, and k-means clustering of columnsrespectively
row_clusters	vector of pre-determined row cluster assignment
col_clusters	vector of pre-determined column cluster assignment
row_k	number of clusters for rows, needed if cluster_rows is kmeans or optional if hclust
col_k	number of clusters for columns, needed if cluster_rows is kmeans or optional if hclust
row_clust_dist	distance function to use for row clustering if hierarchical clustering
col_clust_dist	distance function to use for column clustering if hierarchical clustering
name	Name for colorbar
scale	scale matrix by rows, cols or none
scale_method	what method to use for scaling, either none, standardize, center, normalize
colors	name of RColorBrewer palette or vector of colors for main heatmap
col_clusters_colors	colors for col clusters annotation heatmap
col_clusters_name	name for col clusters colorbar
row_clusters_colors	colors for row clusters annotation heatmap
row_clusters_name	name for row clusters colorbar
show_row_clusters_colorbar	show the colorbar for row clusters?
show_col_clusters_colorbar	show the colorbar for column clusters?
row_annotation	row annotation data.frame
col_annotation	column annotation data.frame
row_annotation_colors	list of colors for row annotations heatmap
col_annotation_colors	list of colors for col annotations heatmap
row_labels	axis labels for y axis
col_labels	axis labels for x axis
row_title	x axis title
col_title	y axis title
colorbar_grid	colorbar grid parameters, should be result from setup_colorbar_grid
layout	list of layout attributes to pass to plotly, eg. list(font = list(size = 15))
source	source name for use with shiny
...	additional argument to iheatmap

Details

By default, no scaling is done of rows or columns. This can be changed by specifying the 'scale' argument. There are three options for scaling methods. "standardize" subtracts the mean and divides by standard deviation, "center" just subtracts the mean, and "normalize" divides by the sum of the values. "normalize" should only be used for data that is all positive! If alternative scaling is desired, the scaling should be done prior to calling the iheatmap function.

Value

Iheatmap-class object, which can be printed to generate an interactive graphic

Author(s)

Alicia Schep

See Also

iheatmap, add_iheatmap, to_widget

Examples

mat <- matrix(rnorm(24), nrow = 6)
annotation = data.frame(gender = c(rep("M", 3),rep("F",3)),
 age = c(20,34,27,19,23,30))
hm <- iheatmap(mat, 
 cluster_rows = "hclust",
 cluster_cols = "kmeans", 
 col_k = 3, 
 row_annotation = annotation)

# Print heatmap if interactive session 
if (interactive()) hm 

iheatmapr documentation built on Aug. 31, 2023, 1:08 a.m.