Select a normalization method

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()
#> ℹ Normalizing quantitative data
#> ℹ ... using scaled shift
#> ✔ ... using scaled shift [122ms]
#> 
#> ℹ ... using median shift
#> ✔ ... using median shift [107ms]
#> 
#> ℹ ... using linear regression
#> ✔ ... using linear regression [207ms]
#> 
#> ℹ ... using limma regression
#> ✔ ... using limma regression [422ms]
#> 
#> ℹ ... using loess regression
#> ✔ ... using loess regression [1.2s]
#> 
#> ℹ ... using randomforest regression
#> ✔ ... using randomforest regression [37.1s]
#> 
#> 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