单细胞数据分析流程和验证实验

1. 分析流程

Basic algorithms

• Expression quantification and quality control
• Normalization (CPM/TPM + logarithm)
• Batch effect correction for integrating multiple datasets
• Feature selection (only keeping highly variable genes)
• Principle Component Analysis (acceleration + denoise)
• Non-linear dimensional reduction for visualization (tSNE/UMAP)
• Unsupervised clustering (K-means/community detection)
• Differential expression (one-vs-rest)
• Annotate clusters based on marker genes

Additional algorithms

• Supervised annotation
• Trajectory inference for continuous cell states
• RNA velocity analysis
• Cell-cell interaction analysis
• Deconvolution analysis for bulk-data
• Integrated analysis with other techniques (scATAC-seq, CITE-seq, spatial-seq, TCR/BCR-seq)

2. 验证实验

参考：加这些单细胞测序验证实验，高分文章稳了！