Gene expression ranking change based single sample pre-disease state detection
IntroductionTo prevent disease, it is of great importance to detect the critical point (pre-disease state) when the biological system abruptly transforms from normal to disease state. However, rapid and accurate pre-disease state detection is still a challenge when there is only a single sample avai...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2024.1509769/full |
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| Summary: | IntroductionTo prevent disease, it is of great importance to detect the critical point (pre-disease state) when the biological system abruptly transforms from normal to disease state. However, rapid and accurate pre-disease state detection is still a challenge when there is only a single sample available. The state transition of the biological system is driven by the variation in regulations between genes.MethodsIn this study, we propose a rapid single-sample pre-disease state-identifying method based on the change in gene expression ranking, which can reflect the coordinated shifts between genes, that is, S-PCR. The R codes of S-PCR can be accessed at https://github.com/ZhenshenBao/S-PCR.ResultsThis model-free method is validated by the successful identification of pre-disease state for both simulated and five real datasets. The functional analyses of the pre-disease state-related genes identified by S-PCR also demonstrate the effectiveness of this computational approach. Furthermore, the time efficiency of S-PCR is much better than that of its peers.DiscussionHence, the proposed S-PCR approach holds immense potential for clinical applications in personalized disease diagnosis. |
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| ISSN: | 1664-8021 |