Plot a heatmap of quantitative values by sample

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 to append to the plot file name

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)

# subset for fast execution
hela_proteins <- hela_proteins %>% subset(description %like% "Ribosome")
#> 
#> ℹ Subsetting data: description %like% Ribosome
#> ✔ Subsetting data: description %like% Ribosome ... done
#> 

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 [20ms]
#> 
#> ℹ ... using median shift
#> ✔ ... using median shift [15ms]
#> 
#> ℹ ... using linear regression
#> ✔ ... using linear regression [25ms]
#> 
#> ℹ ... using limma regression
#> ✔ ... using limma regression [24ms]
#> 
#> ℹ ... using loess regression
#> ✔ ... using loess regression [25ms]
#> 
#> 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