Robust identification of temporal biomarkers in longitudinal omics studies.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
02 08 2022
Historique:
received: 29 11 2021
revised: 28 04 2022
accepted: 26 06 2022
pubmed: 29 6 2022
medline: 15 11 2022
entrez: 28 6 2022
Statut: ppublish

Résumé

Longitudinal studies increasingly collect rich 'omics' data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease transitions. However, the generation of longitudinal omics data has preceded the development of analysis tools that can efficiently extract insights from such data. In particular, there is a need for statistical frameworks that can identify not only which omics features are differentially regulated between groups but also over what time intervals. Additionally, longitudinal omics data may have inconsistencies, including non-uniform sampling intervals, missing data points, subject dropout and differing numbers of samples per subject. In this work, we developed OmicsLonDA, a statistical method that provides robust identification of time intervals of temporal omics biomarkers. OmicsLonDA is based on a semi-parametric approach, in which we use smoothing splines to model longitudinal data and infer significant time intervals of omics features based on an empirical distribution constructed through a permutation procedure. We benchmarked OmicsLonDA on five simulated datasets with diverse temporal patterns, and the method showed specificity greater than 0.99 and sensitivity greater than 0.87. Applying OmicsLonDA to the iPOP cohort revealed temporal patterns of genes, proteins, metabolites and microbes that are differentially regulated in male versus female subjects following a respiratory infection. In addition, we applied OmicsLonDA to a longitudinal multi-omics dataset of pregnant women with and without preeclampsia, and OmicsLonDA identified potential lipid markers that are temporally significantly different between the two groups. We provide an open-source R package (https://bioconductor.org/packages/OmicsLonDA), to enable widespread use. Supplementary data are available at Bioinformatics online.

Identifiants

pubmed: 35762936
pii: 6619161
doi: 10.1093/bioinformatics/btac403
pmc: PMC9344853
doi:

Substances chimiques

Biomarkers 0
Proteins 0

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

3802-3811

Subventions

Organisme : NIDDK NIH HHS
ID : P30 DK020579
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK116074
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR003142
Pays : United States
Organisme : NIH Common Fund Human Microbiome Project (HMP)
ID : 1U54DE02378901

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press.

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Auteurs

Ahmed A Metwally (AA)

Department of Genetics, Stanford University, Stanford, CA 94305, USA.
Illumina Artificial Intelligence Laboratory, Illumina Inc., San Diego, CA 92122, USA.
Systems and Biomedical Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt.

Tom Zhang (T)

Department of Computer Science, Columbia University, New York, NY 10027, USA.

Si Wu (S)

Department of Genetics, Stanford University, Stanford, CA 94305, USA.

Ryan Kellogg (R)

Department of Genetics, Stanford University, Stanford, CA 94305, USA.
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.

Wenyu Zhou (W)

Department of Genetics, Stanford University, Stanford, CA 94305, USA.

Kevin Contrepois (K)

Department of Genetics, Stanford University, Stanford, CA 94305, USA.

Hua Tang (H)

Department of Genetics, Stanford University, Stanford, CA 94305, USA.

Michael Snyder (M)

Department of Genetics, Stanford University, Stanford, CA 94305, USA.

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Classifications MeSH