TiSA: TimeSeriesAnalysis-a pipeline for the analysis of longitudinal transcriptomics data.
Journal
NAR genomics and bioinformatics
ISSN: 2631-9268
Titre abrégé: NAR Genom Bioinform
Pays: England
ID NLM: 101756213
Informations de publication
Date de publication:
Mar 2023
Mar 2023
Historique:
received:
27
09
2022
revised:
12
01
2023
accepted:
24
02
2023
entrez:
7
3
2023
pubmed:
8
3
2023
medline:
8
3
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36879899
doi: 10.1093/nargab/lqad020
pii: lqad020
pmc: PMC9985321
doi:
Types de publication
Journal Article
Langues
eng
Pagination
lqad020Informations de copyright
© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.
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