德国癌症研究中心Oliver Stegle、Britta Velten等研究人员合作利用MEFISTO从多模态数据中识别时空变化的模式。该研究于2022年1月13日在线发表于国际一流学术期刊《自然—方法学》。

研究人员提出了MEFISTO，一个灵活多变的工具，可用于在已知样本之间的空间或时间依赖性的情况下对高维数据进行建模。MEFISTO保持了因子分析对多模态数据的既定优势，但能够进行空间-时间上的降维、插值和平滑与非平滑变化模式的分离。此外，MEFISTO可以通过同时识别和调整潜在的变化模式，并以数据驱动的方式整合多个相关数据集。

为了说明MEFISTO，研究人员将该模型应用于具有空间或时间分辨率的不同数据集，包括器官发育的进化图谱、纵向的微生物组研究、小鼠胃肠发育的单细胞多组学图谱和空间分辨率的转录组学。

据了解，因子分析是基因组生物学中广泛使用的降维方法，其应用范围从个性化健康到单细胞生物学。现有的因子分析模型假设观察到的样本是独立的，这一假设在时空分析研究中是失败的。

附：英文原文

Title: Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO

Author: Velten, Britta, Braunger, Jana M., Argelaguet, Ricard, Arnol, Damien, Wirbel, Jakob, Bredikhin, Danila, Zeller, Georg, Stegle, Oliver

Issue&Volume: 2022-01-13

Abstract: Factor analysis is a widely used method for dimensionality reduction in genome biology, with applications from personalized health to single-cell biology. Existing factor analysis models assume independence of the observed samples, an assumption that fails in spatio-temporal profiling studies. Here we present MEFISTO, a flexible and versatile toolbox for modeling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multimodal data, but enables the performance of spatio-temporally informed dimensionality reduction, interpolation, and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. To illustrate MEFISTO, we apply the model to different datasets with spatial or temporal resolution, including an evolutionary atlas of organ development, a longitudinal microbiome study, a single-cell multi-omics atlas of mouse gastrulation and spatially resolved transcriptomics. MEFISTO models bulk and single-cell multi-omics data with temporal or spatial dependencies for interpretable pattern discovery and integration.

DOI: 10.1038/s41592-021-01343-9

Source: https://www.nature.com/articles/s41592-021-01343-9