Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.

Highlights d diffTF estimates differential TF activity based on chromatin data (basic mode) d Integration with RNA-seq identifies activator/repressor TFs (classification mode) d We applied diffTF to subtypes of CLL and hematopoietic differentiation d The TF classification was experimentally validated in both case studies

Methods for profiling RNA and protein expression in a spatially-resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and to develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Here we propose to group these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed to facilitate further progress in spatial transcriptomics and proteomics.Computational methods are key to extracting patterns from such data and will be especially powerful if they are designed to take into account the specific biological questions at hand as well as the distinct features and limitations of different measurement methods. Here, we review the computational methods for spatial molecular analysis organized by the biological questions they address and the spatial methods capable of measuring relevant parameters. We will focus specifically on the challenges that different length scales pose for experimental methods, and highlight the types of analysis methods that can be deployed in such studies. We define 'length scales' as the spatial context in which a biological process occurs: short-range length scales include direct cell-cell interactions, whereas long-range length scales include global gradients, such as in oxygen or metabolites ( Fig. 1). In addition, we emphasize conceptual overlaps with and distinctions from computational methods currently used for analyzing single cell, dissociation-based methods to show how studies based on single-cell profiling approaches can be complemented by spatial approaches, and vice versa. We hope this conceptual and methodological roadmap will help drive the development of new computational methods for key biological questions in tissue biology, provide guidance to biologists seeking to apply methods, and help in sharpening concepts in cell and tissue biology. Modeling variation at length scales for cell and tissue biologyA long-standing goal in biology is to understand how tissue organization ('structure') relates to tissue physiology ('function'). In a cell-centric model, tissue organization can be described by the different properties (or variables) that distinguish cells from each other (Fig. 1a) . Some of these components can be seen as dependent variables, with which we are able to measure variation between cells, whereas others can be seen as independent variables, with which we are able to explain the observed variation . In dissociated single cell prof...