Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.

单细胞和空间分子分析测定在灵敏度、分辨率和通量方面显示出巨大的进步。将这些技术应用于人类和模型生物体的样本，有望对细胞类型进行全面编目，揭示其发育过程中的谱系起源，并辨别它们对​​疾病发病机制的贡献。此外，成本的迅速下降使得控制良好的扰动实验和队列研究变得广泛可用，阐明了在细胞、组织和整个生物体规模上产生表型的机制。解释即将到来的大量单细胞数据（其中大部分将在空间上解析）将为现有计算管道带来巨大负担。然而，统计概念、模型、工具和算法可以重新利用来解决目前在发育和疾病的遗传和分子生​​物学研究中出现的问题。在这里，我回顾了最近的技术创新有望回答的问题如何通过主要类别的统计工具来解决。