semi_pheatmap: A function to draw clustered heatmaps.
In compbiomed/celda: CEllular Latent Dirichlet Allocation

Description Usage Arguments Details Value Author(s)

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

semi_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(cluster_rows, 50, 0),
  treeheight_col = ifelse(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 = TRUE,
  show_colnames = TRUE, main = NA, fontsize = 10,
  fontsize_row = fontsize, fontsize_col = fontsize,
  display_numbers = FALSE, 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, row_label, col_label, ...)

`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`	Numeric vector. A sequence of numbers that covers the range of values in the normalized 'counts'. Values in the normalized 'matrix' are assigned to each bin in 'breaks'. Each break is assigned to a unique color from 'col'. If NULL, then breaks are calculated automatically. Default NULL.
`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)
`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 shere 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)
`row_label`	row cluster labels for semi-clustering
`col_label`	column cluster labels for semi-clustering
`...`	graphical parameters for the text used in plot. Parameters passed to `grid.text`, see `gpar`.

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.

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

Raivo Kolde <rkolde@gmail.com> #@examples # 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)

# 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 = stats::dist(test, method = "minkowski") dcols = stats::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)

dontrun # Same using dendsort package library(dendsort)

callback = function(hc, ...)dendsort(hc) pheatmap(test, clustering_callback = callback)

compbiomed/celda documentation built on May 25, 2019, 3:58 a.m.