Plot a heatmap of quantitative values by sample
plot_heatmap.Rd
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.
Arguments
- data
tidyproteomics data object
- tag
a character string
- row_names
a boolean
- ...
passthrough for ggsave see
plotting
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 [129ms]
#>
#> ℹ ... using median shift
#> ✔ ... using median shift [143ms]
#>
#> ℹ ... using linear regression
#> ✔ ... using linear regression [222ms]
#>
#> ℹ ... using limma regression
#> ✔ ... using limma regression [582ms]
#>
#> ℹ ... using loess regression
#> ✔ ... using loess regression [1.2s]
#>
#> 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