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)".
- fold.colours
DEFAULT = "Spectre". Character, a custom blue:black:red fold-change colour scheme. Can also be "RdYlBu", "YlGnBu", "viridis", "magma", "inferno", "spectral", "Blues", "Reds", "Greys", or "rev(RdBu)".
- path
DEFAULT = NULL. The location to save plots. By default, will save to current working directory. Can be overidden by entering a sub-directory name (e.g. if you have a subdirectory under your working directory called 'plots', you can enter this here).
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/
Author
Thomas M Ashhurst, thomas.ashhurst@sydney.edu.au Felix Marsh-Wakefield, felix.marsh-wakefield@sydney.edu.au
Examples
# Create some sample data
library(data.table)
dat <- Spectre::demo.clustered
## MFI cluster vs marker heatmap
# mock MFI data per sample, population.
demo.exp <- Spectre::demo.clustered[, lapply(.SD, mean), .SDcols = c("CD45_asinh", "Ly6G_asinh", "CD11b_asinh", "B220_asinh", "CD8a_asinh"), by = Population]
Spectre::make.pheatmap(dat = demo.exp,
file.name = "Expression pheatmap.png",
plot.title = "Expression",
sample.col = "Population",
plot.cols = c("CD45_asinh", "Ly6G_asinh", "CD11b_asinh", "B220_asinh", "CD8a_asinh"))
#> A pheatmap has been saved to your working directoryExpression pheatmap.png
# create mock population count per sample
demo.sum <- Spectre::demo.clustered[, .(count = .N), by = c("Sample", "Population", "Group", "Batch")]
demo.sum <- dcast(demo.sum, Sample+Group+Batch ~ Population, value.var='count')
## Z-scrore of fold-change type heatmap
z.dat <- do.zscore(dat = demo.sum,
use.cols = c("CD4 T cells", "CD8 T cells", "Infil Macrophages", "Microglia", "NK cells", "Neutrophils"),
replace = TRUE)
Spectre::make.pheatmap(dat = z.dat,
file.name = "z-score.png",
plot.title = "z-score",
sample.col = "Sample",
plot.cols = c("CD4 T cells", "CD8 T cells", "Infil Macrophages", "Microglia", "NK cells", "Neutrophils"),
annot.cols = c("Group", "Batch"),
is.fold = TRUE,
fold.range = c(3,-3)
)
#> A pheatmap has been saved to your working directoryz-score.png