So sánh phương pháp

	Phân tích Đa dạng Hệ Vi sinh Vật theo Chuỗi Thời gian	Phân tích biểu hiện gen vi sai theo chuỗi thời gian
Lĩnh vực	Tin sinh học	Tin sinh học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2010s (formalized with 16S amplicon sequencing era; expanded ~2012–2020)	2006–2018 (principal methods established)
Người khởi xướng	Developed iteratively through the microbiome research community; key contributions from Susan Holmes, Rob Knight, and colleagues	Conesa et al. (maSigPro, 2006); extended by Fischer et al. (ImpulseDE2, 2018) and others
Loại	Longitudinal observational / bioinformatics pipeline	Computational genomics pipeline
Công trình gốc	Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583.	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.
Tên gọi khác	longitudinal microbiome diversity analysis, temporal microbiome analysis, repeated-measures microbiome diversity, time-course microbiome analysis	longitudinal RNA-seq DE analysis, temporal transcriptomics, time-course RNA-seq, dynamic DE analysis
Liên quan	5	6
Tóm tắt	Time-series microbiome diversity analysis tracks how the richness, evenness, and community composition of microbial communities change across multiple time points within the same subjects. By combining standard diversity metrics with longitudinal statistical models, it separates true temporal dynamics from inter-individual variation, identifying when and how perturbations such as diet changes, antibiotic treatment, or disease onset reshape the microbiome.	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.