normalize_data: Normalize intensity data

In promor: Proteomics Data Analysis and Modeling Tools

Normalize intensity data

Description

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

Usage

normalize_data(df, method = "quantile")

Arguments

df	An imp_df object with missing values imputed using impute_na or a raw_df object containing missing values.
method	Name of the normalization method to use. Choices are "none", "scale", "quantile" or "cyclicloess." Default is "quantile."

Details

• normalize_data is a wrapper function around the normalizeBetweenArrays function from the limma package.

• 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.

Value

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

Author(s)

Chathurani Ranathunge

See Also

• create_df

• impute_na

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

Examples

## Generate a raw_df object with default settings. No technical replicates.
raw_df <- create_df(
  prot_groups = "https://raw.githubusercontent.com/caranathunge/promor_example_data/main/pg1.txt",
  exp_design = "https://raw.githubusercontent.com/caranathunge/promor_example_data/main/ed1.txt"
)

## 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)

promor documentation built on July 26, 2023, 5:39 p.m.