make.pheatmap: make.pheatmap - Create a 'pretty' heatmap (pheatmap)

In sydneycytometry/Spectre: A computational toolkit in R for the integration, exploration, and analysis of high-dimensional single-cell cytometry data.

Description

This function allows you to create a coloured heatmap for clusters vs markers (coloured by MFI) or clusters vs samples (coloured by MFI or cell counts). Can be set to plot 'fold-change' values (with is.fold = TRUE) that are provided in log2. make.pheatmap is a wrapper around the pheatmap function from pheatmap. Also uses the packages 'RColorBrewer' and 'scales' for colour customisation.

Usage

make.pheatmap(dat, sample.col, plot.cols, annot.cols, file.name, plot.title, transpose, is.fold, fold.range, normalise, dendrograms, row.sep, col.sep, cell.size, standard.colours, path)

Arguments

dat	NO DEFAULT. data.frame. Clusters/populations vs markers (MFI) or Clusters/populations vs samples or (MFI or cell numbers).
sample.col	NO DEFAULT. Character. Specify the name of the column that indicates samples. Will be rows on heatmap.
plot.cols	NO DEFAULT. Character vector. Name of columns you wish to plot on the heatmap. Will be columns on heatmap.
annot.cols	DEFAULT = NULL. Character. Columns which contain values that you do NOT want to plot in the heatmap, e.g. sample name, group name, Live/Dead etc.
file.name	DEFAULT = paste0("Pheatmap by ", sample.col, ".png"). Character. What do you want to call the file, including the extension.
plot.title	DEFAULT = paste0(sample.col, " heatmap"). Character.
transpose	DEFAULT = FALSE. Logical. Do you want to transpose the heatmap.
normalise	DEFAULT = TRUE. Logical. Only applies to standard heatmaps (i.e. when is.fold = FALSE). TRUE to normalise each column between 0 and 1, FALSE to plot the raw values.
is.fold	DEFAULT = FALSE. Logical. TRUE for fold-change heatmap, FALSE for normal values heatmap.
fold.range	DEFAULT = NULL. Numeric vector. For fold-change heatmaps, what is the maxium and minimum values that should be plotted. Example: for a max of 3, and a minimum of -3 would b c(3,-3). Defaults to NULL (which will use the max and min within the dataset).
dendrograms	DEFAULT = "both". Character. Do you want to include dendrograms on columns/rows. Can be "both", "row", "column", or "none.
cutree_rows	DEFAULT = 1 (i.e. no divisions). Divides rows based on the level of dendrogram branches, if dendrograms = 'both' or 'row'.
cutree_cols	DEFAULT = 1 (i.e. no divisions). Divides columns based on the level of dendrogram branches, if dendrograms = 'both' or 'column'
row.sep	DEFAULT = c(). Numeric. Only used if not clustering rows.
col.sep	DEFAULT = c(). Numeric. Only used if not clustering columns
cell.size	DEFAULT = 15. Numeric.
standard.colours	DEFAULT = "BuPu". Character. Can also be "RdYlBu", "YlGnBu", "viridis", "magma", "inferno", "spectral", "Blues", "Reds", "Greys", or "rev(RdBu)".
path	DEFAULT = getwd(). The location to save plots. By default, will save to current working directory. Can be overidden.

Author(s)

Thomas M Ashhurst, [e-mail] Felix Marsh-Wakefield, [e-mail]

References

https://sydneycytometry.org.au/spectre. Helpful examples at https://davetang.org/muse/2018/05/15/making-a-heatmap-in-r-with-the-pheatmap-package/

Examples

## MFI cluster vs marker heatmap
Spectre::make.pheatmap(dat = Spectre::demo.exp,
                       file.name = "Expression pheatmap.png",
                       plot.title = "Expression",
                       sample.col = "Population",
                       plot.cols = names(Spectre::demo.exp)[c(2:10)])

## Z-scrore of fold-change type heatmap
z.dat <- do.zscore(dat = Spectre::demo.sum,
                   use.cols = names(Spectre::demo.sum)[c(4:15)])

Spectre::make.pheatmap(dat = z.dat,
                       file.name = "z-score.png",
                       plot.title = "z-score",
                       sample.col = "Sample",
                       plot.cols = names(z.dat)[c(4:15)],
                       annot.cols = names(z.dat)[c(2:3)],
                       is.fold = TRUE,
                       fold.range = c(3,-3)
                       )

sydneycytometry/Spectre documentation built on March 20, 2021, 2:15 a.m.