Data analysis of scRNA-seq of CD45+ TIICs from DEN + CCI4 HCC tumor

The number of cells used for scRNA-seq of CD45⁺ TIICs in DEN + CCI₄ HCC mice model was 23,150. Single-cell RNAseq data were processed using the Cell Ranger (v.7.0.0) with GRCm38 mouse reference and the downstream analyses were using the filtered gene expression matrices by R software (v.4.0.4) with the Seurat package (v.4.0.5)⁷³. For filtering, after removed the doublet or negative cells using the HTODemux function, genes expressed >3 and cells with >200 genes detected and <25% UMIs derived from the mitochondrial genome were selected for further analyses. For the purpose of identify and compare shared cell type that were present across these samples, we used Harmony⁷⁷ standard workflow for normalization, variable gene selection, integration, dimensionality reduction and clustering as all details can be found in the website tutorial (https://htmlpreview.github.io/?https://github.com/satijalab/seurat.wrappers/blob/master/docs/harmony.html). After cells were clustered, the FindAllMarkers function was used to detect cluster-specific expressed genes, base on which clusters were classified and annotated. Differential gene expression testing was performed using the FindMarkers function in Seurat with Wilcoxon Rank Sum test by default and the Benjamini–Hochberg method was used to estimate the false discovery rate (FDR). DEGs were filtered using a minimum log2 (fold change) of 0.25 and a maximum FDR value of 0.05. Enrichment analysis for the functions of the DEGs was conducted using clusterProfiler package⁷⁸. The Monocle2 package (v2.18.0)⁷⁹ was used to analyze single-cell trajectories in order to discover the cell-state transitions.