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

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  • 1Laboratory of Systems and Synthetic Biology, Wageningen University , Stippeneng 4 6708 HB Wageningen, The Netherlands.

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|December 16, 2016
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Summary
This summary is machine-generated.

Normalization impacts biological data structure, affecting analysis like principal component analysis. This study details how 11 normalization methods alter correlation structures and discusses implications for interpreting results, including metabolite association networks.

Keywords:
COVSCANMRPCAPLS-DAcovariance analysissample-to-sample variationspurious correlationurine dilution

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Analysis

Background:

  • Normalization is crucial for processing biological data, correcting sample variations.
  • Existing normalization methods can alter the underlying data structure.
  • Understanding these alterations is vital for accurate data interpretation.

Purpose of the Study:

  • To investigate the impact of 11 normalization procedures on the correlation structure of biological data.
  • To assess the extent to which normalization affects data structure.
  • To discuss the consequences of these structural changes for downstream data analysis.

Main Methods:

  • Application of 11 distinct normalization procedures to biological datasets.
  • Analysis of the resulting correlation structures.
  • Evaluation of impacts on principal component analysis, partial least-squares discrimination, and network inference.

Main Results:

  • Demonstration that normalization significantly affects the correlation structure of biological data.
  • Quantification of the extent of structural changes induced by different normalization methods.
  • Identification of specific impacts on multivariate statistical analyses and network construction.

Conclusions:

  • Normalization procedures have a substantial and variable effect on biological data's correlation structure.
  • These effects have direct consequences for the interpretation of results from methods like PCA and PLS-DA.
  • Careful consideration of normalization methods is essential for robust biological data analysis and metabolite-metabolite association network inference.