Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands, Division of Molecular Biology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands, Division of Medical Oncology, The Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands and Faculty of EEMCS, Delft University of Technology, 2628 CN, Delft, The Netherlands.
Nucleic Acids Res. 2013 Nov;41(21):e200. doi: 10.1093/nar/gkt845. Epub 2013 Sep 22.
Traditional methods that aim to identify biomarkers that distinguish between two groups, like Significance Analysis of Microarrays or the t-test, perform optimally when such biomarkers show homogeneous behavior within each group and differential behavior between the groups. However, in many applications, this is not the case. Instead, a subgroup of samples in one group shows differential behavior with respect to all other samples. To successfully detect markers showing such imbalanced patterns of differential signal, a different approach is required. We propose a novel method, specifically designed for the Detection of Imbalanced Differential Signal (DIDS). We use an artificial dataset and a human breast cancer dataset to measure its performance and compare it with three traditional methods and four approaches that take imbalanced signal into account. Supported by extensive experimental results, we show that DIDS outperforms all other approaches in terms of power and positive predictive value. In a mouse breast cancer dataset, DIDS is the only approach that detects a functionally validated marker of chemotherapy resistance. DIDS can be applied to any continuous value data, including gene expression data, and in any context where imbalanced differential signal is manifested.
传统方法旨在识别能够区分两组的生物标志物，如 Significance Analysis of Microarrays 或 t 检验，当这些生物标志物在每组内表现出均匀的行为且在组间表现出差异行为时，其性能最佳。然而，在许多应用中，情况并非如此。相反，一组中的一个亚组样本相对于所有其他样本表现出差异行为。为了成功检测显示这种不平衡差异信号的标志物，需要采用不同的方法。我们提出了一种新方法，专门用于检测不平衡差异信号 (DIDS)。我们使用人工数据集和人类乳腺癌数据集来衡量其性能，并将其与三种传统方法和四种考虑不平衡信号的方法进行比较。实验结果表明，DIDS 在功效和阳性预测值方面均优于其他所有方法。在一个小鼠乳腺癌数据集上，DIDS 是唯一检测到化疗耐药的功能验证标志物的方法。DIDS 可应用于任何连续值数据，包括基因表达数据，以及在表现出不平衡差异信号的任何情况下。
