Detection of batch effects in liquid chromatography-mass spectrometry metabolomic data using guided principal component analysis

Autores de IIS La Fe

• Julia Kuligowski

  Autor

• Isabel Lliso Escudero

  Autor

• Justo Javier Escobar Cubiella

  Autor

• Zacarias Leon Gonzalez

  Autor

• Lourdes Gombau Suarez

  Autor

• 

  Máximo Vento Torres

  Autor

• Guillermo Quintas Soriano

  Autor

Participantes ajenos a IIS La Fe

• Pérez-Guaita D
• Solberg R
• Saugstad OD

Grupos

• Perinatología

  

  Leader

  Máximo Vento Torres

Abstract

Metabolomics based on liquid chromatography-mass spectrometry (LC-MS) is a powerful tool for studying dynamic responses of biological systems to different physiological or pathological conditions. Differences in the instrumental response within and between batches introduce unwanted and uncontrolled data variation that should be removed to extract useful information. This work exploits a recently developed method for the identification of batch effects in high throughput genomic data based on the calculation of a delta statistic through principal component analysis (PCA) and guided PCA. Its applicability to LC-MS metabolomic data was tested on two real examples. The first example involved the repeated analysis of 42 plasma samples and 6 blanks in three independent batches, and the second data set involved the analysis of 101 plasma and 18 blank samples in a single batch with a total runtime of 50 h. The first and second data set were used to evaluate between and within-batch effects using the statistic, respectively. Results obtained showed the usefulness of using the delta statistic together with other approaches such as summary statistics of peak intensity distributions, PCA scores plots or the monitoring of IS peak intensities, to detect and identify instrumental instabilities in LC-MS. (C) 2014 Elsevier B.V. All rights reserved.

Keywords

• Liquid chromatography-mass spectrometry (LC-MS); Metabolomics; Guided principal component analysis; Batch effect