So sánh phương pháp

	Phân tích Tiến hóa Tế bào Đơn - Tái tạo Cây Dòng dõi ở Độ phân giải Tế bào Đơn	Phân tích RNA-seq đơn bào
Lĩnh vực	Tin sinh học	Tin sinh học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2014-2020 (rapid development period)	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
Người khởi xướng	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)
Loại	Computational phylogenetic inference pipeline	High-throughput single-cell transcriptomic profiling pipeline
Công trình gốc	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.	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.
Tên gọi khác	scPhylogeny, single-cell lineage tracing, clonal phylogenetics, single-cell tree inference	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
Liên quan	4	5
Tóm tắt	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.