Select a normalization method
select_normalization.Rd
select_normalization()
selects the best normalization method base on low
CVs, low PCA (PC1), and wide Dynamic Range. This is a passthrough function
as it returns the original tidyproteomics data-object.
Examples
library(dplyr, warn.conflicts = FALSE)
library(tidyproteomics)
hela_proteins <- hela_proteins %>%
normalize(.method = c("scaled", "median", "linear", "limma", "loess","randomforest")) %>%
select_normalization()
#> ℹ Normalizing quantitative data
#> ℹ ... using scaled shift
#> ✔ ... using scaled shift [133ms]
#>
#> ℹ ... using median shift
#> ✔ ... using median shift [118ms]
#>
#> ℹ ... using linear regression
#> ✔ ... using linear regression [219ms]
#>
#> ℹ ... using limma regression
#> ✔ ... using limma regression [400ms]
#>
#> ℹ ... using loess regression
#> ✔ ... using loess regression [1.3s]
#>
#> ℹ ... using randomforest regression
#> ✔ ... using randomforest regression [36s]
#>
#> ℹ Selecting best normalization method
#> ✔ Selecting best normalization method ... done
#>
#> ℹ ... selected randomforest
#> ℹ Selecting best normalization method
#> ✔ Selecting best normalization method ... done
#>
#> ℹ ... selected randomforest