Transkingdom Network Analysis (TkNA) : a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions
© 2024. Springer Nature Limited..
We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/.
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
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Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Nature protocols - (2024) vom: 12. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Newman, Nolan K [VerfasserIn] |
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Date Revised 25.03.2024 published: Print-Electronic UpdateOf: bioRxiv. 2023 Mar 29;:. - PMID 36865280 Citation Status Publisher |
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| doi: |
10.1038/s41596-024-00960-w |
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| 520 | |a We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ | ||
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| 700 | 1 | |a Pederson, Jacob W |e verfasserin |4 aut | |
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| 700 | 1 | |a Shan, Jigui |e verfasserin |4 aut | |
| 700 | 1 | |a Williams, Joshua |e verfasserin |4 aut | |
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| 700 | 1 | |a Brown, Kevin |e verfasserin |4 aut | |
| 700 | 1 | |a Morgun, Andrey |e verfasserin |4 aut | |
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