pheatmap (version 1.0.12)

pheatmap: A function to draw clustered heatmaps.

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",
  clustering_callback = identity2, cutree_rows = NA, cutree_cols = NA,
  treeheight_row = ifelse((class(cluster_rows) == "hclust") || cluster_rows,
  50, 0), treeheight_col = ifelse((class(cluster_cols) == "hclust") ||
  cluster_cols, 50, 0), legend = TRUE, legend_breaks = NA,
  legend_labels = NA, annotation_row = NA, annotation_col = NA,
  annotation = NA, annotation_colors = NA, annotation_legend = TRUE,
  annotation_names_row = TRUE, annotation_names_col = TRUE,
  drop_levels = TRUE, show_rownames = T, show_colnames = T, main = NA,
  fontsize = 10, fontsize_row = fontsize, fontsize_col = fontsize,
  angle_col = c("270", "0", "45", "90", "315"), display_numbers = F,
  number_format = "%.2f", number_color = "grey30", fontsize_number = 0.8
  * fontsize, gaps_row = NULL, gaps_col = NULL, labels_row = NULL,
  labels_col = NULL, filename = NA, width = NA, height = NA,
  silent = FALSE, na_col = "#DDDDDD", ...)

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 aggregate 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. When breaks do not cover the range of values, then any value larger than max(breaks) will have the largest color and any value lower than min(breaks) will get the lowest color.

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 or hclust object,

cluster_cols

boolean values determining if columns should be clustered or hclust object.

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.

clustering_callback

callback function to modify the clustering. Is called with two parameters: original hclust object and the matrix used for clustering. Must return a hclust object.

cutree_rows

number of clusters the rows are divided into, based on the hierarchical clustering (using cutree), if rows are not clustered, the argument is ignored

cutree_cols

similar to cutree_rows, but for columns

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_row

data frame that specifies the annotations shown on left side of the heatmap. Each row defines the features for a specific row. The rows in the data and in the annotation are matched using corresponding row names. Note that color schemes takes into account if variable is continuous or discrete.

annotation_col

similar to annotation_row, but for columns.

annotation

deprecated parameter that currently sets the annotation_col if it is missing

annotation_colors

list for specifying annotation_row and annotation_col 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.

annotation_names_row

boolean value showing if the names for row annotation tracks should be drawn.

annotation_names_col

boolean value showing if the names for column 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)

angle_col

angle of the column labels, right now one can choose only from few predefined options (0, 45, 90, 270 and 315)

display_numbers

logical determining if the numeric values are also printed to the cells. If this is a matrix (with same dimensions as original matrix), the contents of the matrix are shown instead of original values.

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 sprintf).

number_color

color of the text

fontsize_number

fontsize of the numbers displayed in cells

gaps_row

vector of row indices that show where to put gaps into heatmap. Used only if the rows are not clustered. See cutree_row to see how to introduce gaps to clustered rows.

gaps_col

similar to gaps_row, but for columns.

labels_row

custom labels for rows that are used instead of rownames.

labels_col

similar to labels_row, but for columns.

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.

silent

do not draw the plot (useful when using the gtable output)

na_col

specify the color of the NA cell in the matrix.

…

graphical parameters for the text used in plot. Parameters passed to grid.text, see gpar.

Value

Invisibly a pheatmap object that is a list with 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

• gtable a gtable object containing the heatmap, can be used for combining the heatmap with other plots

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.

Examples

# NOT RUN {
# Create test matrix
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)

# Show text within cells
pheatmap(test, display_numbers = TRUE)
pheatmap(test, display_numbers = TRUE, number_format = "\%.1e")
pheatmap(test, display_numbers = matrix(ifelse(test > 5, "*", ""), nrow(test)))
pheatmap(test, cluster_row = FALSE, legend_breaks = -1:4, legend_labels = c("0",
"1e-4", "1e-3", "1e-2", "1e-1", "1"))

# Fix cell sizes and save to file with correct size
pheatmap(test, cellwidth = 15, cellheight = 12, main = "Example heatmap")
pheatmap(test, cellwidth = 15, cellheight = 12, fontsize = 8, filename = "test.pdf")

# Generate annotations for rows and columns
annotation_col = data.frame(
                    CellType = factor(rep(c("CT1", "CT2"), 5)), 
                    Time = 1:5
                )
rownames(annotation_col) = paste("Test", 1:10, sep = "")

annotation_row = data.frame(
                    GeneClass = factor(rep(c("Path1", "Path2", "Path3"), c(10, 4, 6)))
                )
rownames(annotation_row) = paste("Gene", 1:20, sep = "")

# Display row and color annotations
pheatmap(test, annotation_col = annotation_col)
pheatmap(test, annotation_col = annotation_col, annotation_legend = FALSE)
pheatmap(test, annotation_col = annotation_col, annotation_row = annotation_row)

# Change angle of text in the columns
pheatmap(test, annotation_col = annotation_col, annotation_row = annotation_row, angle_col = "45")
pheatmap(test, annotation_col = annotation_col, angle_col = "0")

# Specify colors
ann_colors = list(
    Time = c("white", "firebrick"),
    CellType = c(CT1 = "#1B9E77", CT2 = "#D95F02"),
    GeneClass = c(Path1 = "#7570B3", Path2 = "#E7298A", Path3 = "#66A61E")
)

pheatmap(test, annotation_col = annotation_col, annotation_colors = ann_colors, main = "Title")
pheatmap(test, annotation_col = annotation_col, annotation_row = annotation_row, 
         annotation_colors = ann_colors)
pheatmap(test, annotation_col = annotation_col, annotation_colors = ann_colors[2]) 

# Gaps in heatmaps
pheatmap(test, annotation_col = annotation_col, cluster_rows = FALSE, gaps_row = c(10, 14))
pheatmap(test, annotation_col = annotation_col, cluster_rows = FALSE, gaps_row = c(10, 14), 
         cutree_col = 2)

# Show custom strings as row/col names
labels_row = c("", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "Il10", "Il15", "Il1b")

pheatmap(test, annotation_col = annotation_col, labels_row = labels_row)

# 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)

# Modify ordering of the clusters using clustering callback option
callback = function(hc, mat){
    sv = svd(t(mat))$v[,1]
    dend = reorder(as.dendrogram(hc), wts = sv)
    as.hclust(dend)
}

pheatmap(test, clustering_callback = callback)

# }
# NOT RUN {
# Same using dendsort package
library(dendsort)

callback = function(hc, ...){dendsort(hc)}
pheatmap(test, clustering_callback = callback)
# }
# NOT RUN {
# }