How to get the most out of your cancer spatial transcriptomics data

Spatial transcriptomics (ST) has emerged as a powerful tool in cancer research, significantly expanding our capacity to study the complexity of tumour ecosystems. Together with the diversity of ST platforms, a plethora of analysis approaches and tools have been developed with the goal of extracting distinct aspects of biological information contained in the data. From visualizing gene expression in the context of tissue structure and cell morphology, to the exploitation of machine learning and spatial statistics to identify cell neighbourhoods, quantify tumour heterogeneity and map cell-cell signalling networks, there is a current explosion of novel analyses techniques. Unfortunately, this makes it challenging to develop workflows and strategies for data analysis, especially for those new to the field. This review serves to offer a path to cancer researchers who recognise the potential of ST and would like to start their data analysis journey. We cover the main analysis approaches used to address common research questions associated with ST data in cancer, highlighting commonly used tools, as well as discuss emerging analysis techniques that hold the potential to leverage the richness of the data at an unprecedented scale. Finally, we end by highlighting considerations when designing ST projects, from experimental design, to assembling teams and managing the rapid flux of ST technologies. We anticipate this review will be useful resource for researchers to not just seek analysis strategies to answer their current research questions, but also provide inspiration to further take advantage of the wealth of information provided by ST data.