biostudies-literatureUnknown8(4)Kelly RSNational Institute of Allergy and Infectious DiseasesNational Heart, Lung, and Blood InstituteNational Centers for Advancing Translational SciencesNHLBI NIH HHSU.S. Department of DefenseTo explore novel methods for the analysis of metabolomics data, we compared the ability of Partial Least Squares Discriminant Analysis (PLS-DA) and Bayesian networks (BN) to build predictive plasma metabolite models of age three asthma status in 411 three year olds (<i>n</i> = 59 cases and 352 controls) from the Vitamin D Antenatal Asthma Reduction Trial (VDAART) study. The standard PLS-DA approach had impressive accuracy for the prediction of age three asthma with an Area Under the Curve Convex Hull (AUCCH) of 81%. However, a permutation test indicated the possibility of overfitting. In contrast, a predictive Bayesian network including 42 metabolites had a significantly higher AUCCH of 92.1% (<i>p</i> for difference < 0.001), with no evidence that this accuracy was due to overfitting. Both models provided biologically informative insights into asthma; in particular, a role for dysregulated arginine metabolism and several exogenous metabolites that deserve further investigation as potential causative agents. As the BN model outperformed the PLS-DA model in both accuracy and decreased risk of overfitting, it may therefore represent a viable alternative to typical analytical approaches for the investigation of metabolomics data.Metaboliteshttps://www.ebi.ac.uk/biostudies/studies/S-EPMC6316795biostudies-literaturePartial Least Squares Discriminant Analysis and Bayesian Networks for Metabolomic Prediction of Childhood Asthma.PMC63167951R01HL123915-01U54TR001012U01HL0915281R01HL123546-01A1T32 HL0074275T32AI007306-30R01 HL139634P01 HL132825W81XWH-17-1-05335R01HL123915-05Kelly RSLee-Sarwar KALasky-Su JLitonjua AAChu SHVirkud YVWeiss STMcGeachie MJHuang MKachroo PfalsePartial Least Squares Discriminant Analysis and Bayesian Networks for Metabolomic Prediction of Childhood Asthma.To explore novel methods for the analysis of metabolomics data, we compared the ability of Partial Least Squares Discriminant Analysis (PLS-DA) and Bayesian networks (BN) to build predictive plasma metabolite models of age three asthma status in 411 three year olds (<i>n</i> = 59 cases and 352 controls) from the Vitamin D Antenatal Asthma Reduction Trial (VDAART) study. The standard PLS-DA approach had impressive accuracy for the prediction of age three asthma with an Area Under the Curve Convex Hull (AUCCH) of 81%. However, a permutation test indicated the possibility of overfitting. In contrast, a predictive Bayesian network including 42 metabolites had a significantly higher AUCCH of 92.1% (<i>p</i> for difference < 0.001), with no evidence that this accuracy was due to overfitting. Both models provided biologically informative insights into asthma; in particular, a role for dysregulated arginine metabolism and several exogenous metabolites that deserve further investigation as potential causative agents. As the BN model outperformed the PLS-DA model in both accuracy and decreased risk of overfitting, it may therefore represent a viable alternative to typical analytical approaches for the investigation of metabolomics data.2018-01-01T00:00:00Z2018 Oct2021-03-18T08:53:43Z2019-03-26T22:36:08ZS-EPMC63167953036051410.3390/metabo8040068