Main function for normalizing quantitative data in a tidyproteomics data-object
normalize.Rd
normalize()
Main function for normalizing quantitative data from a tidyproteomics
data-object. This is a passthrough function as it returns the original
tidyproteomics data-object with an additional quantitative column labeled with the
normalization method(s) used.
This function can accommodate multiple normalization methods in a single pass, and it is useful for examining normalization effects on data. Often it is adventitious to select a optimal normalization method based on performance.
Usage
normalize(
data,
...,
.method = c("scaled", "median", "linear", "limma", "loess", "svm", "randomforest"),
.cores = 1
)
Arguments
- data
tidyproteomics data object
- ...
use a subset of the data for normalization see
subset()
. This is useful when normalizing against a spike-in set of proteins- .method
character vector of normalization to use
- .cores
number of CPU cores to use for multi-threading
Examples
library(dplyr, warn.conflicts = FALSE)
library(tidyproteomics)
hela_proteins %>%
normalize(.method = c("scaled", "median")) %>%
summary("sample")
#> ℹ Normalizing quantitative data
#> ℹ ... using scaled shift
#> ✔ ... using scaled shift [133ms]
#>
#> ℹ ... using median shift
#> ✔ ... using median shift [128ms]
#>
#> ℹ Selecting best normalization method
#> ✔ Selecting best normalization method ... done
#>
#> ℹ ... selected scaled
#>
#> ── Summary: sample ──
#>
#> sample proteins peptides peptides_unique quantifiable CVs
#> control 7055 66329 58706 0.908 0.15
#> knockdown 7055 66329 58706 0.909 0.13
#>
# normalize between samples according to a subset, then apply to all values
# this would be recommended with a pull-down experiment wherein a conserved
# protein complex acts as the majority content and individual inter-actors
# are of quantitative differentiation
hela_proteins %>%
normalize(!description %like% "Ribosome", .method = c("scaled", "median")) %>%
summary("sample")
#> ! normalization based on 5329 of 5346 identifiers
#> ℹ Normalizing quantitative data
#> ℹ ... using scaled shift
#> ✔ ... using scaled shift [129ms]
#>
#> ℹ ... using median shift
#> ✔ ... using median shift [127ms]
#>
#> ℹ Selecting best normalization method
#> ✔ Selecting best normalization method ... done
#>
#> ℹ ... selected scaled
#>
#> ── Summary: sample ──
#>
#> sample proteins peptides peptides_unique quantifiable CVs
#> control 7055 66329 58706 0.908 0.15
#> knockdown 7055 66329 58706 0.909 0.13
#>