Wang Shu, Myers Amy J, Irvine Edward B, Wang Chuangqi, Maiello Pauline, Rodgers Mark A, Tomko Jaime, Kracinovsky Kara, Borish H Jacob, Chao Michael C, Mugahid Douaa, Darrah Patricia A, Seder Robert A, Roederer Mario, Scanga Charles A, Lin Philana Ling, Alter Galit, Fortune Sarah M, Flynn JoAnne L, Lauffenburger Douglas A
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine and Center for Vaccine Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
bioRxiv. 2024 Oct 30:2024.04.13.589359. doi: 10.1101/2024.04.13.589359.
Analysis of multi-modal datasets can identify multi-scale interactions underlying biological systems, but can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. For example, studies in macaques have shown that BCG vaccination by an intravenous route protects against tuberculosis, correlating with changes across various immune data modes. To eliminate spurious correlations and identify critical immune interactions in a public multi-modal dataset (systems serology, cytokines, cytometry) of vaccinated macaques, we applied Markov Fields (MF), a data-driven approach that explains vaccine efficacy and immune correlations via multivariate network paths, without requiring large numbers of samples (i.e. macaques) relative to multivariate features. Furthermore, we find that integrating multiple data modes with MFs helps to remove spurious connections. Finally, we used the MF to predict outcomes of perturbations at various immune nodes, including a B-cell depletion that induced network-wide shifts without reducing vaccine protection, which we validated experimentally.
对多模态数据集的分析可以识别生物系统潜在的多尺度相互作用，但可能会因通过未映射的生物网络传播的间接影响而受到虚假连接的困扰。例如，对猕猴的研究表明，静脉注射卡介苗可预防结核病，这与各种免疫数据模式的变化相关。为了消除虚假相关性，并在接种疫苗的猕猴的公共多模态数据集（系统血清学、细胞因子、细胞计数）中识别关键的免疫相互作用，我们应用了马尔可夫场（MF），这是一种数据驱动的方法，通过多变量网络路径来解释疫苗效力和免疫相关性，相对于多变量特征而言，不需要大量样本（即猕猴）。此外，我们发现将多种数据模式与马尔可夫场相结合有助于消除虚假连接。最后，我们使用马尔可夫场来预测各种免疫节点处扰动的结果，包括一种B细胞耗竭，它在不降低疫苗保护作用的情况下引起全网络的变化，我们通过实验对其进行了验证。
