  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.
