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时间序列微生物组多样性分析×时间序列 RNA-seq 差异表达×
领域生物信息学生物信息学
方法族Process / pipelineProcess / pipeline
起源年份2010s (formalized with 16S amplicon sequencing era; expanded ~2012–2020)2006–2018 (principal methods established)
提出者Developed iteratively through the microbiome research community; key contributions from Susan Holmes, Rob Knight, and colleaguesConesa et al. (maSigPro, 2006); extended by Fischer et al. (ImpulseDE2, 2018) and others
类型Longitudinal observational / bioinformatics pipelineComputational genomics pipeline
开创性文献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. DOI ↗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. link ↗
别名longitudinal microbiome diversity analysis, temporal microbiome analysis, repeated-measures microbiome diversity, time-course microbiome analysislongitudinal RNA-seq DE analysis, temporal transcriptomics, time-course RNA-seq, dynamic DE analysis
相关56
摘要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.
ScholarGate数据集
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  1. v1
  2. 2 来源
  3. PUBLISHED

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ScholarGate方法对比: Time-series microbiome diversity analysis · Time-series RNA-seq differential expression. 于 2026-06-19 检索自 https://scholargate.app/zh/compare