scDown: A Pipeline for Single-Cell RNA-Seq Downstream Analysis

Abstract

Single-cell transcriptomics data are analyzed using two popular tools, Seurat and Scanpy. Multiple separate tools are used downstream of Seurat and Scanpy cell annotation to study cell differentiation and communication, including cell proportion difference analysis between conditions, pseudotime and trajectory analyses to study cell transition, and cell-cell communication analysis. 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 a published dataset and identified a unique, previously undiscovered 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. scDown can be broadly utilized in downstream analyses of scRNA-seq data, particularly in rare diseases.

Keywords: cell proportion difference analysis; cell–cell communication; pseudotime analysis; single-cell transcriptomics; trajectory analysis.

Figure 1

Flowchart of scDown. The scDown pipeline integrates multiple downstream analyses for scRNA-seq data. It includes automated cell type annotation using Symphony, cell proportion comparison with scProportionTest, cell–cell communication analysis via CellChat, trajectory inference with Monocle3, and RNA velocity analysis with scVelo.

Figure 2

The case study results of single-cell data analysis using the scDown pipeline. (a) RNA velocity vector fields are analyzed and visualized on UMAP embeddings separately for each of the three sample conditions (CNV, ASD, and CON). (b) The UMAPs show OPCs and oligodendrocytes and their pseudotime trajectories inferred by fitting a principal graph within each partition. Cells were ordered in pseudotime based on a root node identified using maximal potency. The density plot shows the distribution of cells across the pseudotime for the complete dataset separated by the conditions. (c) The bar plot compares the relative importance of different signaling pathways between the CNV and CON groups. The x-axis represents “Information Flow”, indicating the absolute communication strength of each pathway and highlighting the most influential pathways mediating communication between the groups. The SPP1 pathway is more enriched in CNV samples compared to ASD samples, whereas the PDGF and RA pathways are more enriched in ASD samples than in CNV samples. (d) This bubble plot illustrates the ligand–receptor pairs involved in the ADGRB signaling pathway. Each bubble represents a ligand–receptor interaction between a pair of cell types, with its size indicating the significance (p-value) of the interaction and its color representing the communication probability.