Correlation Patterns in Experimental Data Are Affected by Normalization Procedures: Consequences for Data Analysis and Network Inference

Normalization is a fundamental step in data processing to account for the sample-to-sample variation observed in biological samples. However, data structure is affected by normalization. In this paper, we show how, and to what extent, the correlation structure is affected by the application of 11 di...

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Bibliographic Details
Published inJournal of proteome research Vol. 16; no. 2; pp. 619 - 634
Main Author Saccenti, Edoardo
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 03.02.2017
Subjects
Animals
Least-Squares Analysis
Metabolome - genetics
Principal Component Analysis
Proteome - chemistry
Proteome - genetics
Proteome - standards
Proteomics - standards
Proteomics - statistics & numerical data
sample-to-sample variation, covariance analysis, spurious correlation, NMR, urine dilution, PCA, PLS-DA, COVSCA
Swine
Urine - chemistry
COVSCA
NMR
sample-to-sample variation
covariance analysis
spurious correlation
urine dilution
PLS-DA
PCA
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Summary:Normalization is a fundamental step in data processing to account for the sample-to-sample variation observed in biological samples. However, data structure is affected by normalization. In this paper, we show how, and to what extent, the correlation structure is affected by the application of 11 different normalization procedures. We also discuss the consequences for data analysis and interpretation, including principal component analysis, partial least-squares discrimination, and the inference of metabolite–metabolite association networks.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1535-3893
1535-3907
DOI:10.1021/acs.jproteome.6b00704