Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.

中文翻译：

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马赛克MPI：跨队列和组学模式的模块化数据集成框架


分子分析的进步促进了大型多模式数据集的生成，这些数据集有可能揭示复杂疾病背后的生物变异的关键轴。然而，提取生物学意义需要能够跨不同群体和数据类型执行马赛克整合的计算策略。在这里，我们提出了mosaicMPI，这是一个框架，用于发现代表细胞类型和状态的低到高分辨率分子程序，并将数据集内部和数据集之间集成到代表生物主题的网络中。使用胶质母细胞瘤中的现有数据集，我们证明了这种方法可以跨多个平台稳健地集成单细胞和批量程序。来自队列的临床和分子注释在统计上传播到这个程序网络上，产生了丰富的生物学主题景观。这使得能够深入了解单个肿瘤样本，系统地探索模式之间的关系，并生成可以快速映射新数据集的参考图。马赛克MPI 可在 https://github.com/MorrissyLab/mosaicMPI 获取。