Delgado-Chaves Fernando M, Spurny Ferdinand, Laske Tanja, Oubounyt Mhaned, Baumbach Jan
Institute for Computational Systems Biology, University of Hamburg, Albert-Einstein-Ring 8-10, 22607, Hamburg, Hamburg, Germany.
Viral Systems Modeling, Leibniz Institute of Virology, Martinistraße 52, 20251, Hamburg, Hamburg, Germany.
BMC Bioinformatics. 2025 May 26;26(1):137. doi: 10.1186/s12859-025-06162-9.
Understanding the molecular mechanisms underlying diseases is crucial for more precise, personalized medicine. Pathway-level differential co-expression analysis, a powerful approach for transcriptomics, identifies condition-specific changes in gene-gene interaction networks, offering targeted insights. However, a key challenge is the lack of robust methods and benchmarks specifically for evaluating algorithms' ability to identify disrupted gene-gene associations across conditions. We introduce DRaCOoN (Differential Regulatory and Co-expression Networks), a Python package and web tool for pathway-level differential co-expression analysis. DRaCOoN uniquely integrates multiple association and differential metrics, with a novel, computationally efficient permutation test for significance assessment. Crucially, DRaCOoN also provides a benchmarking framework for comprehensive method evaluation. Extensive benchmarking on simulated data and three real-world datasets (bone healing, colorectal cancer, and head/neck carcinoma) showed that DRaCOoN, particularly with an entropy-based association measure and the s differential metric, consistently outperforms eight other methods. It remains highly accurate in balanced datasets, robust to varying gene perturbation levels, and identifies biologically relevant regulatory changes. Furthermore, DRaCOoN serves as both a powerful tool and a benchmarking framework for elucidating disease mechanisms from transcriptomics data, advancing precision medicine by uncovering critical gene regulatory alterations.
了解疾病背后的分子机制对于更精确的个性化医疗至关重要。通路水平的差异共表达分析是转录组学的一种强大方法，它可以识别基因 - 基因相互作用网络中特定条件下的变化，提供有针对性的见解。然而，一个关键挑战是缺乏专门用于评估算法识别不同条件下破坏的基因 - 基因关联能力的稳健方法和基准。我们引入了DRaCOoN（差异调控和共表达网络），这是一个用于通路水平差异共表达分析的Python软件包和网络工具。DRaCOoN独特地整合了多个关联和差异指标，并采用了一种新颖的、计算效率高的置换检验进行显著性评估。至关重要的是，DRaCOoN还提供了一个用于全面方法评估的基准框架。在模拟数据和三个真实世界数据集（骨愈合、结直肠癌和头颈癌）上的广泛基准测试表明，DRaCOoN，特别是使用基于熵的关联度量和s差异度量时，始终优于其他八种方法。它在平衡数据集中仍然高度准确，对不同的基因扰动水平具有鲁棒性，并能识别生物学上相关的调控变化。此外，DRaCOoN既可以作为一种强大的工具，也可以作为一个基准框架，用于从转录组学数据中阐明疾病机制，通过揭示关键的基因调控改变来推进精准医学。
