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iheatmap

Source: R/iheatmap.R
iheatmap.Rd

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 class '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

Value

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

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.

See also

iheatmap, add_iheatmap, to_widget

Author

Alicia Schep

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