ArticleNAR genomics and bioinformatics2023
TiSA: TimeSeriesAnalysis-a pipeline for the analysis of longitudinal transcriptomics data.
Article in NAR genomics and bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Finetuning Foundation Models for Temporal Clinical Transcriptomics Data.Bioinformatics (Oxford, England) · 2026Article
- T-ChroNet: Time-aware chromatin network reconstruction to detect dynamic regulatory programs in longitudinal epigenetic dataset.NAR genomics and bioinformatics · 2026Article
- Regulation of lineage reprogramming by dynamic chromatin SUMOylation.Cellular and molecular life sciences : CMLS · 2026Article
- Article
- Integrated bioinformatics analysis of the shared molecular mechanisms between Parkinson's disease and COVID-19.mSphere · 2026Article
- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- Article
- Novel methods for temporally varying gene identification in longitudinal studies reveal bleeding and clotting pathway activation caused by blood draws.Briefings in bioinformatics · 2025Article
- Serotonin Mitigates ColdStress-Induced Damage inInternational journal of molecular sciences · 2025Article
- The tumor microenvironment across four dimensions: assessing space and time in cancer biology.Frontiers in immunology · 2025Review
- A novel biomarker of COVID-19: MMP8 emerged by integrated bulk RNAseq and single-cell sequencing.Scientific reports · 2024Article
- Transcriptomic analysis revealsOncology letters · 2024Article
- Longitudinal whole blood transcriptomic analysis characterizes neutrophil activation and interferon signaling in moderate and severe COVID-19.Scientific reports · 2023Article
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Authors and funding
14 authors.
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Abstract
Improved transcriptomic sequencing technologies now make it possible to perform longitudinal experiments, thus generating a large amount of data. Currently, there are no dedicated or comprehensive methods for the analysis of these experiments. In this article, we describe our TimeSeries Analysis pipeline (TiSA) which combines differential gene expression, clustering based on recursive thresholding, and a functional enrichment analysis. Differential gene expression is performed for both the temporal and conditional axes. Clustering is performed on the identified differentially expressed genes, with each cluster being evaluated using a functional enrichment analysis. We show that TiSA can be used to analyse longitudinal transcriptomic data from both microarrays and RNA-seq, as well as small, large, and/or datasets with missing data points. The tested datasets ranged in complexity, some originating from cell lines while another was from a longitudinal experiment of severity in COVID-19 patients. We have also included custom figures to aid with the biological interpretation of the data, these plots include Principal Component Analyses, Multi Dimensional Scaling plots, functional enrichment dotplots, trajectory plots, and complex heatmaps showing the broad overview of results. To date, TiSA is the first pipeline to provide an easy solution to the analysis of longitudinal transcriptomics experiments.
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Registered trials
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