pheatmap: A function to draw clustered heatmaps.

In COMPASS: Combinatorial Polyfunctionality Analysis of Single Cells

Description

A function to draw clustered heatmaps where one has better control over some graphical parameters such as cell size, etc.

Usage

pheatmap(
  mat,
  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),
  kmeans_k = NA,
  breaks = NA,
  border_color = "grey60",
  cellwidth = NA,
  cellheight = NA,
  scale = "none",
  cluster_rows = TRUE,
  cluster_cols = TRUE,
  clustering_distance_rows = "euclidean",
  clustering_distance_cols = "euclidean",
  clustering_method = "complete",
  treeheight_row = ifelse(cluster_rows, 50, 0),
  treeheight_col = ifelse(cluster_cols, 50, 0),
  legend = TRUE,
  legend_breaks = NA,
  legend_labels = NA,
  annotation = NA,
  annotation_colors = NA,
  annotation_legend = TRUE,
  drop_levels = TRUE,
  show_rownames = TRUE,
  show_colnames = TRUE,
  main = NA,
  fontsize = 10,
  fontsize_row = fontsize,
  fontsize_col = fontsize,
  display_numbers = FALSE,
  number_format = "%.2f",
  fontsize_number = 0.8 * fontsize,
  filename = NA,
  width = NA,
  height = NA,
  row_annotation = NA,
  row_annotation_legend = TRUE,
  row_annotation_colors = NA,
  cytokine_annotation = NA,
  headerplot = NA,
  polar = FALSE,
  order_by_max_functionality = TRUE,
  ...
)

Arguments

mat	numeric matrix of the values to be plotted.
color	vector of colors used in heatmap.
kmeans_k	the number of kmeans clusters to make, if we want to agggregate the rows before drawing heatmap. If NA then the rows are not aggregated.
breaks	a sequence of numbers that covers the range of values in mat and is one element longer than color vector. Used for mapping values to colors. Useful, if needed to map certain values to certain colors, to certain values. If value is NA then the breaks are calculated automatically.
border_color	color of cell borders on heatmap, use NA if no border should be drawn.
cellwidth	individual cell width in points. If left as NA, then the values depend on the size of plotting window.
cellheight	individual cell height in points. If left as NA, then the values depend on the size of plotting window.
scale	character indicating if the values should be centered and scaled in either the row direction or the column direction, or none. Corresponding values are "row", "column" and "none"
cluster_rows	boolean values determining if rows should be clustered,
cluster_cols	boolean values determining if columns should be clustered.
clustering_distance_rows	distance measure used in clustering rows. Possible values are "correlation" for Pearson correlation and all the distances supported by dist, such as "euclidean", etc. If the value is none of the above it is assumed that a distance matrix is provided.
clustering_distance_cols	distance measure used in clustering columns. Possible values the same as for clustering_distance_rows.
clustering_method	clustering method used. Accepts the same values as hclust.
treeheight_row	the height of a tree for rows, if these are clustered. Default value 50 points.
treeheight_col	the height of a tree for columns, if these are clustered. Default value 50 points.
legend	logical to determine if legend should be drawn or not.
legend_breaks	vector of breakpoints for the legend.
legend_labels	vector of labels for the legend_breaks.
annotation	data frame that specifies the annotations shown on top of the columns. Each row defines the features for a specific column. The columns in the data and rows in the annotation are matched using corresponding row and column names. Note that color schemes takes into account if variable is continuous or discrete.
annotation_colors	list for specifying annotation track colors manually. It is possible to define the colors for only some of the features. Check examples for details.
annotation_legend	boolean value showing if the legend for annotation tracks should be drawn.
drop_levels	logical to determine if unused levels are also shown in the legend
show_rownames	boolean specifying if column names are be shown.
show_colnames	boolean specifying if column names are be shown.
main	the title of the plot
fontsize	base fontsize for the plot
fontsize_row	fontsize for rownames (Default: fontsize)
fontsize_col	fontsize for colnames (Default: fontsize)
display_numbers	logical determining if the numeric values are also printed to the cells.
number_format	format strings (C printf style) of the numbers shown in cells. For example "%.2f" shows 2 decimal places and "%.1e" shows exponential notation (see more in gettextf).
fontsize_number	fontsize of the numbers displayed in cells
filename	file path where to save the picture. Filetype is decided by the extension in the path. Currently following formats are supported: png, pdf, tiff, bmp, jpeg. Even if the plot does not fit into the plotting window, the file size is calculated so that the plot would fit there, unless specified otherwise.
width	manual option for determining the output file width in inches.
height	manual option for determining the output file height in inches.
row_annotation	data frame that specifies the annotations shown on the rows. Each row defines the features for a specific row. The rows in the data and rows in the annotation are matched using corresponding row names.The category labels are given by the data frame column names.
row_annotation_legend	same interpretation as the column parameters.
row_annotation_colors	same interpretation as the column parameters
cytokine_annotation	A data.frame of factors, with either levels 0 = unexpressed, 1 = expressed, or optionally with a third level -1 = 'left out'. of the categories for each column. They will be colored by their degree of functionality and ordered by degree of functionality and by amount of expression if column clustering is not done.
headerplot	is a list with two components, order and data. Order tells how to reorder the columns of the matrix.
polar	Boolean; if TRUE we draw a polar legend. Primarily for internal use. Data is some summary statistic over the columns which will be plotted in the header where the column cluster tree usually appears. Cytokine ordering is ignored when the headerplot argument is passed.
order_by_max_functionality	Boolean; re-order the cytokine labels by maximum functionality?
...	graphical parameters for the text used in plot. Parameters passed to grid.text, see gpar.

Details

The function also allows to aggregate the rows using kmeans clustering. This is advisable if number of rows is so big that R cannot handle their hierarchical clustering anymore, roughly more than 1000. Instead of showing all the rows separately one can cluster the rows in advance and show only the cluster centers. The number of clusters can be tuned with parameter kmeans_k.

Value

Invisibly a list of components

• tree_row the clustering of rows as hclust object

• tree_col the clustering of columns as hclust object

• kmeans the kmeans clustering of rows if parameter kmeans_k was specified

Author(s)

Original version by Raivo Kolde <[e-mail]>, with modifications by Greg Finak <[e-mail]> and Kevin Ushey <[e-mail]>.

Examples

 # Generate some data
test = matrix(rnorm(200), 20, 10)
test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3
test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2
test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4
colnames(test) = paste("Test", 1:10, sep = "")
rownames(test) = paste("Gene", 1:20, sep = "")

# Draw heatmaps
pheatmap(test)
pheatmap(test, kmeans_k = 2)
pheatmap(test, scale = "row", clustering_distance_rows = "correlation")
pheatmap(test, color = colorRampPalette(c("navy", "white", "firebrick3"))(50))
pheatmap(test, cluster_row = FALSE)
pheatmap(test, legend = FALSE)
pheatmap(test, display_numbers = TRUE)
pheatmap(test, display_numbers = TRUE, number_format = "%.1e")
pheatmap(test, cluster_row = FALSE, legend_breaks = -1:4, legend_labels = c("0",
"1e-4", "1e-3", "1e-2", "1e-1", "1"))
pheatmap(test, cellwidth = 15, cellheight = 12, main = "Example heatmap")
#pheatmap(test, cellwidth = 15, cellheight = 12, fontsize = 8, filename = "test.pdf")


# Generate column annotations
annotation = data.frame(Var1 = factor(1:10 %% 2 == 0,
                             labels = c("Class1", "Class2")), Var2 = 1:10)
annotation$Var1 = factor(annotation$Var1, levels = c("Class1", "Class2", "Class3"))
rownames(annotation) = paste("Test", 1:10, sep = "")

pheatmap(test, annotation = annotation)
pheatmap(test, annotation = annotation, annotation_legend = FALSE)
pheatmap(test, annotation = annotation, annotation_legend = FALSE, drop_levels = FALSE)

# Specify colors
Var1 = c("navy", "darkgreen")
names(Var1) = c("Class1", "Class2")
Var2 = c("lightgreen", "navy")

ann_colors = list(Var1 = Var1, Var2 = Var2)

#Specify row annotations
row_ann <- data.frame(foo=gl(2,nrow(test)/2),`Bar`=relevel(gl(2,nrow(test)/2),"2"))
rownames(row_ann)<-rownames(test)
pheatmap(test, annotation = annotation, annotation_legend = FALSE,
    drop_levels = FALSE,row_annotation = row_ann)

#Using cytokine annotations
M<-matrix(rnorm(8*20),ncol=8)
row_annotation<-data.frame(A=gl(4,nrow(M)/4),B=gl(4,nrow(M)/4))
eg<-expand.grid(factor(c(0,1)),factor(c(0,1)),factor(c(0,1)))
colnames(eg)<-c("IFNg","TNFa","IL2")
rownames(eg)<-apply(eg,1,function(x)paste0(x,collapse=""))
rownames(M)<-1:nrow(M)
colnames(M)<-rownames(eg)
cytokine_annotation=eg
pheatmap(M,annotation=annotation,row_annotation=row_annotation,
     annotation_legend=TRUE,row_annotation_legend=TRUE,
    cluster_rows=FALSE,cytokine_annotation=cytokine_annotation,cluster_cols=FALSE)

# Specifying clustering from distance matrix
drows = dist(test, method = "minkowski")
dcols = dist(t(test), method = "minkowski")
pheatmap(test, clustering_distance_rows = drows, clustering_distance_cols = dcols)

Example output

COMPASS documentation built on Nov. 8, 2020, 8:05 p.m.