The rapid development of sequencing technology has generated complex, highly skewed, and zero-inflated multi-source count data. This has posed significant challenges in variable selection, which is crucial for uncovering shared disease mechanisms, such as tumor development and metabolic dysregulation. In this study, we propose a novel variable selection method called Zero-Inflated Poisson-Gamma based Simultaneous knockoff (ZIPG-SK) for multi-source count data. To address the highly skewed and zero-inflated properties of count data, we introduce a Gaussian copula based on the ZIPG distribution for constructing knockoffs, while also incorporating the information of covariates. This method successfully detects common features related to the results in multi-source data while controlling the false discovery rate (FDR). Additionally, our proposed method effectively combines e-values to enhance power. Extensive simulations demonstrate the superiority of our method over Simultaneous Knockoff and other existing methods in processing count data, as it improves power across different scenarios. Finally, we validated the method by applying it to two real-world multi-source datasets: colorectal cancer (CRC) and type 2 diabetes (T2D). The identified variable characteristics are consistent with existing studies and provided additional insights.

翻译：测序技术的快速发展产生了复杂、高度偏态且零膨胀的多源计数数据。这对变量选择提出了重大挑战，而变量选择对于揭示如肿瘤发展和代谢失调等共享疾病机制至关重要。在本研究中，我们提出了一种名为基于零膨胀泊松-伽马分布的同步Knockoff（ZIPG-SK）的新变量选择方法，用于处理多源计数数据。为应对计数数据的高度偏态和零膨胀特性，我们引入了一种基于ZIPG分布的高斯copula来构建Knockoff，同时整合了协变量的信息。该方法在控制错误发现率（FDR）的同时，成功检测出与多源数据结果相关的共同特征。此外，我们提出的方法有效地结合了e值以提升检验效能。大量的模拟实验表明，我们的方法在处理计数数据时优于同步Knockoff及其他现有方法，因为它在不同场景下均提升了检验效能。最后，我们通过将该方法应用于两个真实世界的多源数据集——结直肠癌（CRC）和2型糖尿病（T2D）——验证了其有效性。所识别出的变量特征与现有研究一致，并提供了新的见解。