Omics studies attempt to extract meaningful messages from large-scale and high-dimensional data sets by treating the data sets as a whole. The concept of treating data sets as a whole is important in every step of the data-handling procedures: the pre-processing step of data records, the step of statistical analyses and machine learning, translation of the outputs into human natural perceptions, and acceptance of the messages with uncertainty. In the pre-processing, the method by which to control the data quality and batch effects are discussed. For the main analyses, the approaches are divided into two types and their basic concepts are discussed. The first type is the evaluation of many items individually, followed by interpretation of individual items in the context of multiple testing and combination. The second type is the extraction of fewer important aspects from the whole data records. The outputs of the main analyses are translated into natural languages with techniques, such as annotation and ontology. The other technique for making the outputs perceptible is visualization. At the end of this review, one of the most important issues in the interpretation of omics data analyses is discussed. Omics studies have a large amount of information in their data sets, and every approach reveals only a very restricted aspect of the whole data sets. The understandable messages from these studies have unavoidable uncertainty.

中文翻译：

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组学数据分析的解释。

Omics研究试图通过将数据集作为一个整体来从大规模和高维数据集中提取有意义的消息。在整个数据处理过程的每个步骤中，将数据集作为整体对待的概念非常重要：数据记录的预处理步骤，统计分析和机器学习步骤，将输出转换为人的自然感知以及接受不确定的消息。在预处理中，讨论了控制数据质量和批处理效果的方法。对于主要分析，将方法分为两种类型并讨论其基本概念。第一种类型是对多个项目进行单独评估，然后在多次测试和组合的情况下对单个项目进行解释。第二种类型是从整个数据记录中提取较少的重要方面。主要分析的输出通过注释和本体等技术转换为自然语言。使输出可感知的另一种技术是可视化。在本文的最后，讨论了在组学数据分析中最重要的问题之一。Omics研究在其数据集中拥有大量信息，并且每种方法仅揭示了整个数据集中非常有限的一个方面。这些研究中可理解的信息具有不可避免的不确定性。使输出可感知的另一种技术是可视化。在本文的最后，讨论了在组学数据分析中最重要的问题之一。Omics研究在其数据集中拥有大量信息，并且每种方法仅揭示了整个数据集中非常有限的一个方面。这些研究中可理解的信息具有不可避免的不确定性。使输出可感知的另一种技术是可视化。在本文的最后，讨论了在组学数据分析中最重要的问题之一。Omics研究在其数据集中拥有大量信息，每种方法都仅揭示了整个数据集中非常有限的一个方面。这些研究中可理解的信息具有不可避免的不确定性。