Plot a heatmap of quantitative values by sample — plot

plot_heatmap() is a pheatmap implementation for plotting the commonly visualized quantitative heatmap according to sample. Both the samples and the quantitative values are clustered and visualized.

Usage

plot_heatmap(data = NULL, tag = NULL, row_names = FALSE, ...)

Arguments

data: tidyproteomics data object
tag: a character string
row_names: either FALSE, or an annotation (eg. protein, gene_name, description etc.)
...: passthrough for ggsave see plotting

Value

a (tidyproteomics data-object | ggplot-object)

Examples

library(dplyr, warn.conflicts = FALSE)
library(tidyproteomics)
hela_proteins %>%
  normalize(.method = c("scaled", "median", "linear", "limma", "loess")) %>%
  select_normalization() %>%
  plot_heatmap()
#> ℹ Normalizing quantitative data
#> ℹ ... using scaled shift
#> ✔ ... using scaled shift [119ms]
#> 
#> ℹ ... using median shift
#> ✔ ... using median shift [134ms]
#> 
#> ℹ ... using linear regression
#> ✔ ... using linear regression [218ms]
#> 
#> ℹ ... using limma regression
#> ✔ ... using limma regression [453ms]
#> 
#> ℹ ... using loess regression
#> ✔ ... using loess regression [1.4s]
#> 
#> ℹ Selecting best normalization method
#> ✔ Selecting best normalization method ... done
#> 
#> ℹ  ... selected loess
#> ℹ Selecting best normalization method
#> ✔ Selecting best normalization method ... done
#> 
#> ℹ  ... selected loess