Single-cell RNA-seq Data Analysis Pipeline

According to the standardized pipeline, the 10 × scRNA-seq data were processed through R software, “Seurat” package. Quality control (QC) was performed on the raw matrix to filter low-quality cells according to the following criteria to obtain a high-quality scRNA-seq expression matrix: (1) only genes that were expressed in at least three single cells and cells that expressed more than 250 genes were selected to create a Seurat object; (2) only cells that expressed more than 500 genes and less than 6000 genes were included; (3) the percentage of mitochondrial or ribosomal genes of each cell was calculated and cells that expressed more than 35% of mitochondrial genes were regarded as low-quality cells and were excluded from downstream analysis. Besides, the “LogNormalize” method in the “NormalizeData” function was used to normalize the scRNA-seq data and the “FindVariableFeatures” function was adopted to filter the top 2000 highly variable genes after QC. Subsequently, the “RunPCA” function in the “Seurat” package was utilized for principal component analysis (PCA) based on the 2000 genes, and the first 15 PCs were chosen for cell clustering analysis. After that, the “FindNeighbors” and “FindClusters” function in the “Seurat” package was adopted for cell clustering identification, with the parameter “resolution” being set as 0.1. Furthermore, uniform manifold approximation and projection (UMAP)^{25 (link)} was used for dimensionality reduction and cluster identification. Then, the “FindAllMarkers” function was exploited to identify significant differentially expressed genes (DEGs) of each cluster by calculating the log₂ [Foldchange (FC)] and the adjusted P-value. DEGs with |log₂FC|≥ 1 and adjusted P-value < 0.05 were considered marker genes of each cluster. The “DotPlot” and “DoHeatmap” function in the “Seurat” package was also adopted to visualize the expression patterns of the top five marker genes in different clusters. Ultimately, R software, “SingleR” package^{26 (link)} was employed for automatically cluster annotation to identify the cell types by referring to the Human Primary Cell Atlas. The R software, “UCell” and “irGSEA” packages were used to accomplish single-cell Gene Set Enrichment Analysis (GSEA). The “monocle” package^{27 (link)} was adopted for cell trajectory and pseudo-time analysis, with the method “DDRTree” being used for dimensionality reduction. Subsequently, the statistical method “BEAM” was used to calculate the contribution of genes during cell development, and the top 100 genes were selected for visualization. Ultimately, R software, “CellChat”^{28 (link)} package was adopted for cell–cell communication network construction. The detailed method was described in the previously published study²⁹ .