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| 单细胞系统发育分析× | Single-cell RNA-seq analysis× | |
|---|---|---|
| 领域 | 生物信息学 | 生物信息学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2014-2020 (rapid development period) | 2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016 |
| 提出者≠ | Multiple groups; foundational tools: Trapnell et al. (Monocle, 2014), Jones et al. (Cassiopeia, 2020) | Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015) |
| 类型≠ | Computational phylogenetic inference pipeline | High-throughput single-cell transcriptomic profiling pipeline |
| 开创性文献≠ | Jones, M. G., Khodaverdian, A., Quinn, J. J., Chan, M. M., Hussmann, J. A., Wang, R., Xu, C., Weissman, J. S., & Yosef, N. (2020). Inference of single-cell phylogenies from lineage tracing data using Cassiopeia. Genome Biology, 21(1), 92. DOI ↗ | Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5), 495–502. DOI ↗ |
| 别名 | scPhylogeny, single-cell lineage tracing, clonal phylogenetics, single-cell tree inference | scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling |
| 相关≠ | 4 | 5 |
| 摘要≠ | Single-cell phylogenetic analysis reconstructs evolutionary or developmental trees from single-cell sequencing data, tracing how individual cells diverged from a common ancestor. By leveraging somatic mutations, CRISPR-introduced barcodes, or copy-number changes as heritable characters, this method maps clonal relationships within tumors, developing tissues, or immune repertoires with unprecedented cellular resolution. | Single-cell RNA sequencing (scRNA-seq) analysis characterises gene expression at the resolution of individual cells, enabling discovery of cell types, states, and transitions that are invisible in bulk transcriptomics. Starting from raw sequencing reads, the workflow produces a cell-by-gene count matrix and proceeds through quality control, normalisation, dimensionality reduction, unsupervised clustering, cell-type annotation, and a range of downstream analyses such as trajectory inference and differential expression between cell populations. |
| ScholarGate数据集 ↗ |
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