English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Mass spectrometry-based metabolomic analysis depends upon the identificationof spectral peaks by their mass and retention time. Statistical analysis thatfollows the identification currently relies on one main peak of each compound.However, a compound present in the sample typically produces several spectralpeaks due to its isotopic properties and the ionization process of the massspectrometer device. In this work, we investigate the extent to which theseadditional peaks can be used to increase the statistical strength ofdifferential analysis.  We present a Bayesian approach for integrating data of multiple detectedpeaks that come from one compound. We demonstrate the approach through asimulated experiment and validate it on ultra performance liquidchromatography-mass spectrometry (UPLC-MS) experiments for metabolomics andlipidomics. Peaks that are likely to be associated with one compound can beclustered by the similarity of their chromatographic shape. Changes ofconcentration between sample groups can be inferred more accurately whenmultiple peaks are available.  When the sample-size is limited, the proposed multi-peak approach improvesthe accuracy at inferring covariate effects. An R implementation, data and thesupplementary material are available athttp://research.ics.aalto.fi/mi/software/peakANOVA/ .

Stronger findings from mass spectral data through multi-peak modeling