So sánh phương pháp

	Phân tích biểu hiện gen vi sai theo chuỗi thời gian	Phân tích RNA-seq đơn bào
Lĩnh vực	Tin sinh học	Tin sinh học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2006–2018 (principal methods established)	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
Người khởi xướng	Conesa et al. (maSigPro, 2006); extended by Fischer et al. (ImpulseDE2, 2018) and others	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)
Loại	Computational genomics pipeline	High-throughput single-cell transcriptomic profiling pipeline
Công trình gốc	Conesa, A., Nueda, M. J., Ferrer, A., & Talon, M. (2006). maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics, 22(9), 1096–1102.	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	longitudinal RNA-seq DE analysis, temporal transcriptomics, time-course RNA-seq, dynamic DE analysis	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
Liên quan	6	5
Tóm tắt	Time-series RNA-seq differential expression analysis identifies genes whose expression levels change systematically across ordered time points — such as during development, disease progression, or response to a treatment. Unlike two-condition DE analysis, it explicitly models the temporal structure of the data, capturing dynamic gene expression trajectories rather than a single snapshot contrast. Tools such as maSigPro, ImpulseDE2, and splineTimeR have been developed specifically for this design.	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.