pheatmap {COMPASS} | R Documentation

## 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",
      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 <rkolde@gmail.com>, with modifications by Greg Finak <gfinak@fhcrc.org> and Kevin Ushey <kushey@fhcrc.org>.

### 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)

[Package COMPASS version 1.30.0 Index]