利用空间基因组学与结构化方法解答生物学中的开放性问题
Answering open questions in biology using spatial genomics and structured methods
AI总结：
本文提出利用空间基因组学数据结合统计与机器学习方法，解答四个此前无法检验的生物学开放问题，涵盖细胞形态、位置、运动及信号传导，并强调通过比较数据与概念模型来检验理论。
摘要：
基因组学方法已在多种生物系统中揭示出规律，但忽略了细胞行为的重要方面：细胞在空间中的形状、相对位置、运动以及细胞间的相互作用。在保留空间信息的同时收集基因组或表观基因组数据的空间技术，已开始克服这些局限性。这些新数据有望更深入地理解影响细胞行为的因素，特别是能够直接检验先前关于细胞状态和变异在形态、位置、运动性和信号传导背景下的现有理论，而这些理论此前无法检验。空间基因组学技术在分辨率、易用性和规模上的快速进步，以解决这些问题，也需要更新一套用于探究这些数据的统计方法工具包。我们提出了四个开放的生物学问题，现在可以利用空间基因组学数据结合分析方法来解答这些问题。我们概述了每种可能产生特定见解的空间数据模态，讨论了如何通过将数据与生物行为的概念模型进行比较来检验相互冲突的理论，并强调了可能特别有助于恢复生物学见解的统计和基于机器学习的工具。
英文摘要：
Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shape, relative locations of, movement of, and interactions between cells in space. Spatial technologies that collect genomic or epigenomic data while preserving spatial information have begun to overcome these limitations. These new data promise a deeper understanding of the factors that affect cellular behavior, and in particular the ability to directly test existing theories about cell state and variation in the context of morphology, location, motility, and signaling that could not be tested before. Rapid advancements in resolution, ease-of-use, and scale of spatial genomics technologies to address these questions also require an updated toolkit of statistical methods with which to interrogate these data. We present four open biological questions that can now be answered using spatial genomics data paired with methods for analysis. We outline spatial data modalities for each that may yield specific insight, discuss how conflicting theories may be tested by comparing the data to conceptual models of biological behavior, and highlight statistical and machine learning-based tools that may prove particularly helpful to recover biological insight.
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