Create heatmap from tidy data — tidyheatmap • tidyheatmaps

A tidyverse-style interface to the powerful heatmap package pheatmap. It enables the convenient generation of complex heatmaps from tidy data.

Usage

tidyheatmap(
  df,
  rows,
  columns,
  values,
  colors = NA,
  color_legend_n = 15,
  color_legend_min = NA,
  color_legend_max = NA,
  color_na = "#DDDDDD",
  annotation_row = NULL,
  annotation_col = NULL,
  gaps_row = NULL,
  gaps_col = NULL,
  show_selected_row_labels = NULL,
  show_selected_col_labels = NULL,
  filename = NA,
  scale = "none",
  fontsize = 7,
  cellwidth = NA,
  cellheight = NA,
  cluster_rows = FALSE,
  cluster_cols = FALSE,
  border_color = NA,
  kmeans_k = NA,
  clustering_distance_rows = "euclidean",
  clustering_distance_cols = "euclidean",
  clustering_method = "complete",
  clustering_callback = function(x, ...) {
     return(x)
 },
  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_colors = NA,
  annotation_legend = TRUE,
  annotation_names_row = TRUE,
  annotation_names_col = TRUE,
  drop_levels = TRUE,
  show_rownames = TRUE,
  show_colnames = TRUE,
  main = NA,
  fontsize_row = fontsize,
  fontsize_col = fontsize,
  angle_col = c("270", "0", "45", "90", "315"),
  display_numbers = FALSE,
  number_format = "%.2f",
  number_color = "grey30",
  fontsize_number = 0.8 * fontsize,
  width = NA,
  height = NA,
  silent = FALSE
)

Arguments

df: A tidy dataframe in long format.
rows, columns: Column in the dataframe to use for heatmap rows and columns.
values: Column in the dataframe containing the values to be color coded in the heatmap cells.
colors: Vector of colors used for the color legend.
color_legend_n: Number of colors in the color legend.
color_legend_min, color_legend_max: Min and max value of the color legend. Values smaller then the color_legend_min will have the lowest color, values bigger than the color_legend_max will get the highest color.
color_na: Color to use for NAs in values.
annotation_row, annotation_col: Column(s) in the dataframe to use for row and column annotation. To use multiple columns for annotation combine then by c(column1, column2).
gaps_row, gaps_col: Column in the dataframe to use for use for row and column gaps.
show_selected_row_labels, show_selected_col_labels: Only display a subset of selected labels for rows and columns. Provide selected labels as c("label1", "label2").
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.
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"
fontsize: base fontsize for the plot
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.
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.
border_color: color of cell borders on heatmap, use NA if no border should be drawn.
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.
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_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_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
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)

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

Examples

# Basic example
tidyheatmap(data_exprs,
            rows = external_gene_name,
            columns = sample,
            values = expression,
            scale = "row"
)


# Change number of colors in color lengend
tidyheatmap(data_exprs,
            rows = external_gene_name,
            columns = sample,
            values = expression,
            scale = "row",
            color_legend_n = 5
)


# Change color in color legend
tidyheatmap(data_exprs,
            rows = external_gene_name,
            columns = sample,
            values = expression,
            scale = "row",
            colors = c("#145afc","#ffffff","#ee4445")
)


# Add row and column annotation
tidyheatmap(data_exprs,
            rows = external_gene_name,
            columns = sample,
            values = expression,
            scale = "row",
            annotation_col = c(sample_type, condition, group),
            annotation_row = c(is_immune_gene, direction)
)


# Add gaps between rows and columns
tidyheatmap(data_exprs,
            rows = external_gene_name,
            columns = sample,
            values = expression,
            scale = "row",
            annotation_col = c(sample_type, condition, group),
            annotation_row = c(is_immune_gene, direction),
            gaps_row = direction,
            gaps_col = group
)