Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece.
GRIB (IMIM/UPF), PRBB, Barcelona, Spain.
Methods Mol Biol. 2023;2571:71-81. doi: 10.1007/978-1-0716-2699-3_7.
Human diseases account for complex traits that usually exhibit markedly diverse clinical manifestations coming from a series of pathogenic processes that shape heterogeneous phenotypes. Considering that correlation does not imply causation as well as population differences and/or inter-individual variability, disease-specific signatures are becoming critical for biomarker discovery. Untargeted metabolomics is deemed to be a powerful approach to delineate molecular pathways of prime interest. Metabotypes capture the interplay of genomics and environmental influences per se. Untargeted metabolomics share the charm of being not only hypothesis-driven but also hypothesis-generating. Notwithstanding, the applicability of untargeted metabolomics toward clinically relevant outcomes depend on wet- and dry-lab procedures in the context of elegant study designs with clear rationale. As ideal may be far from feasible, herein we provide recommendations to combat sample mishandling that adversely affect data outcomes and if so, deal with imbalanced datasets toward data integrity.
人类疾病属于复杂性状，通常表现出明显不同的临床表现，这些临床表现来自一系列导致表型异质性的致病过程。由于相关性并不意味着因果关系以及人群差异和/或个体间的可变性，因此疾病特异性特征对于生物标志物的发现变得至关重要。非靶向代谢组学被认为是描绘主要关注的分子途径的有力方法。代谢型本身捕捉了基因组和环境影响的相互作用。非靶向代谢组学不仅具有假设驱动的特点，而且具有生成假设的特点。尽管如此，非靶向代谢组学在临床相关结果中的应用取决于湿实验室和干实验室程序，以及具有明确基本原理的优雅研究设计。因为理想可能远远不切实际，所以在这里，我们提供了一些建议来应对会对数据结果产生不利影响的样本处理不当的问题，如果出现这种情况，我们将处理数据集不平衡问题，以确保数据的完整性。
