组学技术可生成细胞和组织中生物分子的全面概况。然而，对底层系统的整体理解需要对多种数据模式进行联合分析。我们提出了 DPM，一种使用基因、转录本或蛋白质的方向性和显着性估计来整合组学数据集的数据融合方法。 DPM 允许用户定义输入数据集如何根据实验设计或数据集之间的生物学关系进行定向交互。 DPM 优先考虑在数据集中变化一致的基因和通路，并惩罚那些方向性不一致的基因和通路。为了证明我们的方法，我们通过联合分析转录组、蛋白质组和 DNA 甲基化数据集来表征 IDH 突变神经胶质瘤的基因和通路调控。卵巢癌生存信息的定向整合揭示了在转录物和蛋白质表达中具有一致预后信号的候选生物标志物。 DPM 是一个通用且适应性强的框架，用于多组学数据集中的基因优先级排序和路径分析。© 2024。作者。

Omics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of underlying systems requires joint analyses of multiple data modalities. We present DPM, a data fusion method for integrating omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally given the experimental design or biological relationships between the datasets. DPM prioritises genes and pathways that change consistently across the datasets and penalises those with inconsistent directionality. To demonstrate our approach, we characterise gene and pathway regulation in IDH-mutant gliomas by jointly analysing transcriptomic, proteomic, and DNA methylation datasets. Directional integration of survival information in ovarian cancer reveals candidate biomarkers with consistent prognostic signals in transcript and protein expression. DPM is a general and adaptable framework for gene prioritisation and pathway analysis in multi-omics datasets.© 2024. The Author(s).