A new pipeline for the normalization and pooling of metabolomics data

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Sp...

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Бібліографічні деталі
Автори: Viallon, V, His, M, Rinaldi, S, Breeur, M, Gicquiau, A, Hemon, B, Overvad, K, Tjønneland, A, Rostgaard-Hansen, AL, Rothwell, JA, Lecuyer, L, Severi, G, Kaaks, R, Johnson, T, Schulze, MB, Palli, D, Agnoli, C, Panico, S, Tumino, R, Ricceri, F, Verschuren, WMM, Engelfriet, P, Onland-Moret, C, Vermeulen, R, Nøst, TH, Urbarova, I, Zamora-Ros, R, Rodriguez-Barranco, M, Amiano, P, Huerta, JM, Ardanaz, E, Melander, O, Ottoson, F, Vidman, L, Rentoft, M, Schmidt, JA, Travis, RC, Weiderpass, E, Johansson, M, Dossus, L, Jenab, M, Gunter, MJ, Lorenzo Bermejo, J, Scherer, D, Salek, RM, Keski-Rahkonen, P, Ferrari, P
Формат: Journal article
Мова:English
Опубліковано: MDPI 2021

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