Automatic Normalization

This function automatically selects and applies the most suitable normalization method for the given dataset. If Quality Control (QC) samples are present, the method that best stabilizes them (i.e., yields the lowest median coefficient of variation) is chosen. Otherwise, it defaults to median normalization for glycoproteomics data, and a combination of median quotient and total area normalization for glycomics data.

Usage

auto_normalize(
  exp,
  group_col = "group",
  qc_name = "QC",
  to_try = NULL,
  info = NULL
)

Arguments

exp

An glyexp::experiment().

group_col

The column name in sample_info for groups. Default is "group". Can be NULL when no group information is available.

qc_name

The name of QC samples in the group_col column. Default is "QC". Only used when group_col is not NULL.

to_try

Normalization functions to try. A list. Default includes:

• normalize_median(): median normalization

• normalize_median_abs(): absolute median normalization

• normalize_total_area(): total area mormalization

• normalize_quantile(): quantile normalization

• normalize_loessf(): LoessF normalization

• normalize_loesscyc(): LoessCyc normalization

• normalize_median_quotient(): median quitient normalization

• normalize_rlr(): Robust Linear Regression normalization

• normalize_rlrma(): Robust Linear Regression with Median Adjustment normalization

• normalize_rlrmacyc(): Robust Linear Regression with Median Adjustment and Cyclic normalization

info

Internal parameter used by auto_clean().

Value

The normalized experiment.

Details

By default, all normalization methods except for VSN are included for benchmarking. VSN is excluded because it compresses fold change estimate significantly thus not suitable for regular omics context.

Examples

library(glyexp)
exp_normed <- auto_normalize(real_experiment)
#> No QC samples found. Using default normalization method based on experiment
#> type.
#> Experiment type is "glycoproteomics". Using `normalize_median()`.