Liang Sun, Data Science Manager, Boston Children's Hospital

Single-cell transcriptomics data are analyzed using two popular tools, Seurat and Scanpy. Downstream analyses of annotated cell types such as cell proportion difference analysis between conditions, pseudotime and trajectory analyses to study cell transition, and cell-cell communication analysis are performed to study cell differentiation and communication. To automate the integrative cell differentiation and communication analyses of single-cell RNA-seq data, we developed a single-cell RNA-seq Downstream Analysis pipeline called 'scDown'. This R package includes cell proportion difference analysis, cell-cell communication analysis, pseudotime analysis, and RNA velocity analysis. Both Seurat and Scanpy annotated single-cell RNA-seq data are accepted in this pipeline. We applied scDown to one published dataset and identified a unique signature of neuronal inflammatory signaling associated with a rare genetic neurodevelopmental disorder. These findings were not identified with a simple implementation of Seurat differential gene expression analysis, illustrating the value of our pipeline in biological discovery. This R package is freely available under the MIT license at https://github.com/BCH-RC/scDown.