EIGENMS: EigenMS normalization

In idrblab/NOREVA: R Package for Systematic Optimization of Metabolomic Data Processing

EigenMS normalization

Description

EigenMS is an adaptation of surrogate variable analysis, which identifies trends attributable to bias by utilizing singular value decomposition on model residuals. See the details at the following References.

Usage

EIGENMS(data,label)

Arguments

data	Input matrix of data
label	Input the label of data

Value

A structure with multiple components

m_logInts

number of metabolites x number of samples matrix of expression data with no missing values

grps

either a single factor indicating the treatment group of each sample i.e. [1 1 1 1 2 2 2 2...] or a data frame of factors

m_ints_eig1

First portion of EigenMS: Identify eigentrends attributable to bias

m_ints_norm1

Eliminate the effects of systematic bias identified in eig_norm1()

mm_eigenMS

matrix of normalized abundances, no extra columns

References

1. Metabolomics data normalization with EigenMS. Karpievitch YK, Nikolic SB, Wilson R, Sharman JE, Edwards LM. 2014, PLoS ONE.

2. Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition. Karpievitch YV, Taverner T et al. 2009, Bioinformatics.

Examples

data(mm_metabolites)
head(mm_metabolites)
# different from parameter names as R uses outer name spaces
# if variable is undefined
intsCols = 8:13
metaCols = 1:7 # reusing this variable
m_logInts = make_intencities(mm_metabolites, intsCols)  # will reuse the name
m_logInts = convert_log2(m_logInts)
# 3 samples for CG and 3 for mCG
grps = as.factor(c('CG','CG','CG', 'mCG','mCG','mCG'))
mm_eigenMS = EIGENMS(m_logInts,grps)

idrblab/NOREVA documentation built on April 17, 2025, 2:04 p.m.