Unlike traditional bulk RNA-seq analysis which is dominated by Bioconductor, packages for analysing single-cell RNA sequencing data are more fragmented. Currently, there are three key ecosystems, the Seurat package (available from CRAN), Bioconductor’s SingleCellExperiment object and the AnnData Python object used by the Scanpy package. While these platforms each have strengths and weaknesses most analysts are likely to only use one of them. In this talk, I discuss how interoperability between R and Python can allow us to take advantage of these platforms strengths and avoid unnecessary reimplementation of methods. I highlight the reticulate R package for interacting with Python, the basilisk package for encapsulating Python environments, my zellkonverter package for converting between AnnData and SingleCellExperiment objects and the velociraptor package as an example of wrapping a Python package. The methods in the scVelo (package wrapped by velociraptor) for calculating RNA velocity and the CellRank package for estimating state transitions will also be briefly described.