Identification of cell-cell interactions from high-resolution spatial transcriptomics data
Develop an open-source Python package and publish it on the Python Package Index (PyPI)
Martin Hemberg, PhD
CCB works with the Hemberg Lab on developing a computational pipeline for the identification of cell-cell interactions, with the goal of utilizing the improved spatial resolution provided by the newest generation of commercial technologies. The pipeline implements a method for ligand-receptor analysis named spatially-constrained optimal transport interaction analysis (SCOTIA). The idea of the method is to first identify spatially proximal cell clusters, and to then solve an optimal transport problem between spatially proximal source and target clusters. The goal of the project is to develop a software package that allows for the straightforward application of the method to differential setups such as the comparison of sample groups, eg. representing different experimental conditions.