从多研究数据中精准推断基因共表达网络，抗噪声干扰。
Robust Multi-view Co-expression Network Inference
- 基于多元t分布与稀疏精度矩阵建模跨研究基因表达
- 可识别共表达矩阵（仅缩放因子不确定）
- 适合处理批次效应和虚假相关性的基因网络研究
跨越多个独立研究解析基因共表达有助于理解细胞过程。从转录组数据推断基因共表达网络面临诸多挑战，包括虚假基因相关性、样本相关性及批次效应。为此，我们提出一种高维图推断的鲁棒方法，基于各数据集本质上是服从多元t分布且具有稀疏精度矩阵的基因载荷的噪声线性混合这一前提，该稀疏精度矩阵在各研究间共享。由此可证明，可在其他模型参数下唯一识别出共表达矩阵（仅缩放因子不确定）。方法采用期望-最大化算法进行参数估计。在合成数据与真实基因表达数据上的实证评估表明，相比基线方法，本方法在学习底层图结构方面表现更优。
原文摘要 · Abstract (English)
Unraveling the co-expression of genes across studies enhances the understanding of cellular processes. Inferring gene co-expression networks from transcriptome data presents many challenges, including spurious gene correlations, sample correlations, and batch effects. To address these complexities, we introduce a robust method for high-dimensional graph inference from multiple independent studies. We base our approach on the premise that each dataset is essentially a noisy linear mixture of gene loadings that follow a multivariate $t$-distribution with a sparse precision matrix, which is shared across studies. This allows us to show that we can identify the co-expression matrix up to a scaling factor among other model parameters. Our method employs an Expectation-Maximization procedure for parameter estimation. Empirical evaluation on synthetic and gene expression data demonstrates our method's improved ability to learn the underlying graph structure compared to baseline methods.
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