TY - JOUR
T1 - Investigating sources of variability in metabolomic data in the EPIC study: the Principal Component Partial R-square (PC-PR2) method
AU - Fages, A.
AU - Ferrari, P.
AU - Monni, S.
AU - Dossus, L.
AU - Floegel, A.
AU - Mode, N.
AU - Johansson, M.
AU - Travis, R.C.
AU - Bamia, C.
AU - Boshuizen, H.C.
PY - 2014
Y1 - 2014
N2 - The key goal of metabolomic studies is to identify relevant individual biomarkers or composite metabolic patterns associated with particular disease status or patho-physiological conditions. There are currently very few approaches to evaluate the variability of metabolomic data in terms of characteristics of individuals or aspects pertaining to technical processing. To address this issue, a method was developed to identify and quantify the contribution of relevant sources of variation in metabolomic data prior to investigation of etiological hypotheses. The Principal Component Partial R-square (PC-PR2) method combines features of principal component and of multivariable linear regression analyses. Within the European Prospective Investigation into Cancer and nutrition (EPIC), metabolic profiles were determined by 1H NMR analysis on 807 serum samples originating from a nested liver cancer case–control study. PC-PR2 was used to quantify the variability of metabolomic profiles in terms of study subjects age, sex, body mass index, country of origin, smoking status, diabetes and fasting status, as well as factors related to sample processing. PC-PR2 enables the evaluation of important sources of variations in metabolomic studies within large-scale epidemiological investigations.
AB - The key goal of metabolomic studies is to identify relevant individual biomarkers or composite metabolic patterns associated with particular disease status or patho-physiological conditions. There are currently very few approaches to evaluate the variability of metabolomic data in terms of characteristics of individuals or aspects pertaining to technical processing. To address this issue, a method was developed to identify and quantify the contribution of relevant sources of variation in metabolomic data prior to investigation of etiological hypotheses. The Principal Component Partial R-square (PC-PR2) method combines features of principal component and of multivariable linear regression analyses. Within the European Prospective Investigation into Cancer and nutrition (EPIC), metabolic profiles were determined by 1H NMR analysis on 807 serum samples originating from a nested liver cancer case–control study. PC-PR2 was used to quantify the variability of metabolomic profiles in terms of study subjects age, sex, body mass index, country of origin, smoking status, diabetes and fasting status, as well as factors related to sample processing. PC-PR2 enables the evaluation of important sources of variations in metabolomic studies within large-scale epidemiological investigations.
KW - Epidemiology
KW - European prospective investigation on cancer and nutrition
KW - Metabolomics
KW - Nuclear magnetic resonance
KW - Principal component analysis
KW - Systematic variation
U2 - 10.1007/s11306-014-0647-9
DO - 10.1007/s11306-014-0647-9
M3 - Article
SN - 1573-3882
VL - 10
SP - 1074
EP - 1083
JO - Metabolomics
JF - Metabolomics
IS - 6
ER -