Multivariate data validation for investigating primary HCMV infection in pregnancy

Luigi Barberini, Antonio Noto, Luca Saba, Francesco Palmas, Vassilios Fanos, Angelica Dessì, Maurizio Zavattoni, Claudia Fattuoni, Michele Mussap

Research output: Contribution to journalArticlepeer-review

Abstract

We reported data concerning the Gas Chromatography–Mass Spectrometry (GC–MS) based metabolomic analysis of amniotic fluid (AF) samples obtained from pregnant women infected with Human Cytomegalovirus (HCMV). These data support the publication “Primary HCMV Infection in Pregnancy from Classic Data towards Metabolomics: an Exploratory analysis” (C. Fattuoni, F. Palmas, A. Noto, L. Barberini, M. Mussap, et al., 2016) [2]. GC–MS and Multivariate analysis allow to recognize the molecular phenotype of HCMV infected fetuses (transmitters) and that of HCMV non-infected fetuses (non-transmitters); moreover, GC–MS and multivariate analysis allow to distinguish and to compare the molecular phenotype of these two groups with a control group consisting of AF samples obtained in HCMV non-infected pregnant women. The obtained data discriminate controls from transmitters as well as from non-transmitters; no statistically significant difference was found between transmitters and non-transmitters.

Original languageEnglish
Pages (from-to)220-230
Number of pages11
JournalData in Brief
Volume9
DOIs
Publication statusPublished - Dec 1 2016

Keywords

  • Amniotic fluid
  • Cross validation performance
  • Cytomegalovirus
  • least square discriminant (PLS-DA) analysis
  • Metabolomics
  • Multivariate statistical approach
  • Partial
  • Pregnancy

ASJC Scopus subject areas

  • General

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