scPlant: a versatile framework for single-cell transcriptomic data analysis in plants.

Single-cell transcriptomics has fully been embraced in plant biological research and is revolutionizing our understanding of plant growth, development, and responses to external stimuli. However, single-cell transcriptomic data analysis in plants is not trivial given that there is no end-to-end solution so far and integration of various bioinformatics tools is heavyweight in terms of a number of required dependencies. Here, we present scPlant, a versatile framework for exploring plant single-cell atlases with minimum input data provided by users. The scPlant pipeline is implemented with plentiful functions for diverse analytical tasks, ranging from basic data processing to advanced demands such as cell type annotation and deconvolution, trajectory inference, cross-species data integration and cell-type specific gene regulatory network construction. In addition, a variety of visualization tools are bundled in a build-in Shiny application, allowing for single-cell transcriptomic data exploration on the fly. scPlant is freely available at https://github.com/compbioNJU/scPlant for non-commercial purposes.