单细胞转录组：解码基因差异之谜

单细胞转录组差异基因分析

单细胞转录组测序（scRNA-seq）能够解析细胞间的异质性，差异基因分析（Differential Gene Expression, DGE）是识别不同细胞群体间关键基因表达变化的核心方法。

数据预处理
原始数据需经过质量控制（QC）、标准化（如CPM、TPM或DESeq2的size factor校正）及批次效应校正（如Harmony或Seurat的CCA）。低质量细胞和基因需过滤，通常保留线粒体基因占比低、检测到足够基因数的细胞。

差异分析方法
常见工具包括MAST、DESeq2（单细胞适配版）、limma和Wilcoxon秩和检验（Seurat默认方法）。MAST适用于零膨胀数据，DESeq2适合高测序深度样本，而Wilcoxon检验对稀疏数据稳健。

关键参数设置
需调整logFC阈值（通常≥0.25）和p值校正方法（如FDR或Bonferroni）。伪批量分析（pseudobulk）可提升统计效力，尤其适用于稀有细胞类型比较。

富集分析

差异基因列表需通过功能富集分析解释其生物学意义，常用方法包括基因本体（GO）、KEGG通路和GSEA（基因集富集分析）。

数据库选择
GO分析涵盖分子功能（MF）、生物过程（BP）和细胞组分（CC）。KEGG提供通路级注释，MSigDB包含更广泛的基因集（如Hallmark集合）。

工具与流程
ClusterProfiler（R包）支持GO/KEGG富集，GSEA需排名基因（按logFC或p值）。结果可视化采用点图、网络图或富集地图（EnrichmentMap）。

注意事项
避免冗余术语（Revigo可去冗余），关注保守通路（如代谢或信号转导）。跨物种分析需基因ID转换（如biomaRt）。

整合分析示例

以Seurat流程为例：

1. 差异分析：

markers <- FindMarkers(seurat_obj, ident.1 = "Cluster1", ident.2 = "Cluster2", test.use = "wilcox")  

2. 富集分析（ClusterProfiler）：

go_result <- enrichGO(gene = markers$gene, OrgDb = org.Hs.eg.db, ont = "BP")  
dotplot(go_result)  

通过差异基因与富集结果的结合，可揭示细胞亚群的特异功能及其在疾病或发育中的潜在作用。

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