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 %>% subset(description %like% "Ribosome")
#>
#> ℹ Subsetting data: description %like% Ribosome
#> ✔ Subsetting data: description %like% Ribosome ... done
#>
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 [14ms]
#>
#> ℹ ... using median shift
#> ✔ ... using median shift [15ms]
#>
#> ℹ ... using linear regression
#> ✔ ... using linear regression [26ms]
#>
#> ℹ ... using limma regression
#> ✔ ... using limma regression [18ms]
#>
#> ℹ ... using loess regression
#> ✔ ... using loess regression [24ms]
#>
#> ℹ ... using randomforest regression
#> ✔ ... using randomforest regression [43ms]
#>
#> 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