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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

a boolean

...

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()
#>  Normalizing quantitative data
#>  ... using scaled shift
#>  ... using scaled shift [129ms]
#> 
#>  ... using median shift
#>  ... using median shift [143ms]
#> 
#>  ... using linear regression
#>  ... using linear regression [222ms]
#> 
#>  ... using limma regression
#>  ... using limma regression [582ms]
#> 
#>  ... using loess regression
#>  ... using loess regression [1.2s]
#> 
#>  Selecting best normalization method
#>  Selecting best normalization method ... done
#> 
#>   ... selected loess
#>  Selecting best normalization method
#>  Selecting best normalization method ... done
#> 
#>   ... selected loess