By Nick Eagles
We’ve recently been interested in exploring the (largely python-based) tools others have published to process spatial transcriptomics data for various end goals. A common goal is to integrate data from platforms like Visium, which provides some information about how gene expression is spatially organized, with other approaches with potentially better spatial resolution or gene throughput. In particular, we came across a paper by Biancalani, Scalia et al.