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This function normalizes data using a user-specified normalization method.


normalize_data(df, method = "quantile")



An imp_df object with missing values imputed using impute_na or a raw_df object containing missing values.


Name of the normalization method to use. Choices are "none", "scale", "quantile" or "cyclicloess." Default is "quantile."


A norm_df object, which is a data frame of normalized protein intensities.


  • This function normalizes intensity values to achieve consistency among samples.

  • It assumes that the intensities in the data frame have been log-transformed, therefore, it is important to make sure that create_df was run with log_tr = TRUE(default) when creating the raw_df object.

See also

  • impute_na

  • See normalizeBetweenArrays in the R package limma for more information on the different normalization methods available.


Chathurani Ranathunge


## Generate a raw_df object with default settings. No technical replicates.
raw_df <- create_df(
  prot_groups = "",
  exp_design = ""
#> 0 empty row(s) removed.
#> 0 empty column(s) removed.
#> 80 protein(s) (rows) only identified by site removed.
#> 65 reverse protein(s) (rows) removed.
#> 42 protein potential contaminant(s) (rows) removed.
#> 1923 protein(s) identified by 2 or fewer unique peptides removed.
#> Zeros have been replaced with NAs.
#> Data have been log-transformed.

## Impute missing values in the data frame using the default minProb
## method prioir to normalization.
imp_df <- impute_na(raw_df)

## Normalize the imp_df object using the default quantile method
norm_df1 <- normalize_data(imp_df)

## Use the cyclicloess method
norm_df2 <- normalize_data(imp_df, method = "cyclicloess")

## Normalize data in the raw_df object prior to imputation.
norm_df3 <- normalize_data(raw_df)