Skip to contents

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.

Usage

select_normalization(data = NULL, normalization = NULL)

Arguments

data

tidyproteomics data object

normalization

a character string

Value

a 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()
#>  Normalizing quantitative data
#>  ... using scaled shift
#>  ... using scaled shift [133ms]
#> 
#>  ... using median shift
#>  ... using median shift [118ms]
#> 
#>  ... using linear regression
#>  ... using linear regression [219ms]
#> 
#>  ... using limma regression
#>  ... using limma regression [400ms]
#> 
#>  ... using loess regression
#>  ... using loess regression [1.3s]
#> 
#>  ... using randomforest regression
#>  ... using randomforest regression [36s]
#> 
#>  Selecting best normalization method
#>  Selecting best normalization method ... done
#> 
#>   ... selected randomforest
#>  Selecting best normalization method
#>  Selecting best normalization method ... done
#> 
#>   ... selected randomforest