Université de Nantes, Centre Hospitalier Universitaire Nantes, Institute of Health and Medical Research, Centre de Recherche en Transplantation et Immunologie, UMR 1064, Institut de Transplantation Urologie-Néphrologie, Nantes, France.
Département Informatique et Mathématiques, Ecole Centrale de Nantes, Nantes, France.
Transplantation. 2022 Feb 1;106(2):e114-e125. doi: 10.1097/TP.0000000000003992.
In both research and care, patients, caregivers, and researchers are facing a leap forward in the quantity of data that are available for analysis and interpretation, marking the daunting "big data era." In the biomedical field, this quantitative shift refers mostly to the -omics that permit measuring and analyzing biological features of the same type as a whole. Omics studies have greatly impacted transplantation research and highlighted their potential to better understand transplant outcomes. Some studies have emphasized the contribution of omics in developing personalized therapies to avoid graft loss. However, integrating omics data remains challenging in terms of analytical processes. These data come from multiple sources. Consequently, they may contain biases and systematic errors that can be mistaken for relevant biological information. Normalization methods and batch effects have been developed to tackle issues related to data quality and homogeneity. In addition, imputation methods handle data missingness. Importantly, the transplantation field represents a unique analytical context as the biological statistical unit is the donor-recipient pair, which brings additional complexity to the omics analyses. Strategies such as combined risk scores between 2 genomes taking into account genetic ancestry are emerging to better understand graft mechanisms and refine biological interpretations. The future omics will be based on integrative biology, considering the analysis of the system as a whole and no longer the study of a single characteristic. In this review, we summarize omics studies advances in transplantation and address the most challenging analytical issues regarding these approaches.
在研究和护理中，患者、护理人员和研究人员都面临着可供分析和解释的数据量的飞跃，这标志着令人畏惧的“大数据时代”的到来。在生物医学领域，这种数量上的转变主要指的是可以测量和分析同一类型生物特征的组学。组学研究极大地影响了移植研究，并强调了它们在更好地理解移植结果方面的潜力。一些研究强调了组学在开发个性化治疗以避免移植物丢失方面的贡献。然而，在分析过程中，整合组学数据仍然具有挑战性。这些数据来自多个来源。因此，它们可能包含偏差和系统误差，这些误差可能被误认为是相关的生物学信息。已经开发了标准化方法和批次效应来解决与数据质量和同质性相关的问题。此外，插补方法处理数据缺失。重要的是，移植领域代表了一个独特的分析环境，因为生物学统计单位是供体-受者对，这给组学分析带来了额外的复杂性。诸如结合考虑遗传起源的 2 个基因组之间的风险评分等策略正在涌现，以更好地理解移植物机制并完善生物学解释。未来的组学将基于综合生物学，考虑整个系统的分析，而不再是单个特征的研究。在这篇综述中，我们总结了移植组学研究的进展，并讨论了这些方法最具挑战性的分析问题。
