In many biomedical applications with high-dimensional features, such as single-cell RNA-sequencing, it is not uncommon to observe numerous structural zeros. Identifying important features from a pool of high-dimensional data for subsequent detailed analysis is often of interest. Here, we describe an exact, rapid Bayesian screening approach with attractive diagnostic properties, utilizing a Tweedie model. The method provides the likelihood that a feature with structural zeros merits further investigation, as well as distributions of the effect magnitudes and the proportion of features with the same expected responses under alternative conditions. The method is agnostic to assay, data type, and application. Through numerical studies, we demonstrate that the proposed methodology is effective in identifying important features for follow-up experimentation across a range of applications, including single-cell differential expression analysis of embryonic stem cells and embryonic fibroblasts in mice and differential analysis of CD4 and CD8 Peripheral Blood Mononuclear Cells (PBMCs) in humans.

翻译：在许多具有高维特征的生物医学应用中，例如单细胞RNA测序，观测到大量结构零值的情况并不罕见。从高维数据池中识别重要特征以进行后续详细分析通常是研究关注的重点。本文提出了一种基于Tweedie模型的精确、快速的贝叶斯筛选方法，该方法具有优异的诊断特性。该方法能够提供具有结构零值的特征值得进一步研究的似然度，以及效应大小分布和不同条件下具有相同预期响应的特征比例分布。本方法不依赖于检测技术、数据类型或具体应用领域。通过数值研究，我们证明所提出的方法能有效识别多种应用场景中值得后续实验验证的重要特征，包括小鼠胚胎干细胞与胚胎成纤维细胞的单细胞差异表达分析，以及人类CD4与CD8外周血单个核细胞的差异分析。