apply_norm: Apply normalization based on different published methods

In m-jahn/R-tools: Utility and wrapper functions for bioinformatics work

Apply normalization based on different published methods

Description

This function is a wrapper applying different normalization functions from other packages, such as 'limma', 'justvsm' and 'preprocesscore'. These are not imported automatically but have to be installed separately. Alternatively, it will apply any custom normalization functions that is passed to the 'norm_function' argument.

Usage

apply_norm(
  data,
  norm_function = "none",
  sample_cols = 1:ncol(data),
  ref_cols = NULL
)

Arguments

data	the input data frame
norm_function	(character) the normalization function to be applied (one of "none", "normalizeMedianValues", "normalize.quantiles", "normalize.quantiles.robust", "normalize.quantiles.in.blocks", "justvsn")
sample_cols	(numeric) The sample columns of the input data frame to be normalized. Default is to use all columns
ref_cols	(numeric) The reference to be normalized to. Does not work with all functions. The default is NULL

Details

This function provides several normalization functions to re-scale the peptide quantities of a sample to improve comparability and remove systematic bias, like differences in total sample concentration or sample load.

A very simple normalization is based on aligning the median of all samples. More sophisticated normalization functions include VSN normalization that returns log2 transformed data. This is back-transformed to be compatible with subsequent peptide aggregation.

More normalization functions are taken from package preProcessCore that offers quantile normalization, robust quantile normalization, or block quantile normalization. Quantile normalization uses a rank based approach where intensities are rescaled between minimum, median and maximum. Rank-based normalizations don't keep original difference between intensities within one sample. Therefore it is not advisable to use quantile-normalized data for relative comparison within a sample.

Value

a normalized data frame

Examples

df <- data.frame(
  protein = LETTERS[1:5],
  cond1 = sample(1:100, 5),
  cond2 = sample(1:100, 5),
  cond3 = sample(1:100, 5)
)

# normalize protein abundance to obtain identical median;
# function borrowed from limma::normalizeMedianValues()
median_norm <- function(x) {
  cmed <- log(apply(x, 2, median, na.rm = TRUE))
  cmed <- exp(cmed - mean(cmed))
  t(t(x)/cmed)
}

df_norm <- apply_norm(
  df, 
  norm_function = median_norm, 
  sample_cols = 2:ncol(df),
  ref_cols = NULL
)

# the data after normalization
print(df_norm)

# Has the normalization worked? We can compare column medians
# for original and normalized data
apply(df[2:4], 2, median)
apply(df_norm[2:4], 2, median)

m-jahn/R-tools documentation built on Feb. 5, 2023, 1:05 p.m.