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New generative methods for single-cell transcriptome data in bulk RNA sequence deconvolution.
- Source :
-
Scientific reports [Sci Rep] 2024 Feb 20; Vol. 14 (1), pp. 4156. Date of Electronic Publication: 2024 Feb 20. - Publication Year :
- 2024
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Abstract
- Numerous methods for bulk RNA sequence deconvolution have been developed to identify cellular targets of diseases by understanding the composition of cell types in disease-related tissues. However, issues of heterogeneity in gene expression between subjects and the shortage of reference single-cell RNA sequence data remain to achieve accurate bulk deconvolution. In our study, we investigated whether a new data generative method named sc-CMGAN and benchmarking generative methods (Copula, CTGAN and TVAE) could solve these issues and improve the bulk deconvolutions. We also evaluated the robustness of sc-CMGAN using three deconvolution methods and four public datasets. In almost all conditions, the generative methods contributed to improved deconvolution. Notably, sc-CMGAN outperformed the benchmarking methods and demonstrated higher robustness. This study is the first to examine the impact of data augmentation on bulk deconvolution. The new generative method, sc-CMGAN, is expected to become one of the powerful tools for the preprocessing of bulk deconvolution.<br /> (© 2024. The Author(s).)
Details
- Language :
- English
- ISSN :
- 2045-2322
- Volume :
- 14
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Scientific reports
- Publication Type :
- Academic Journal
- Accession number :
- 38378978
- Full Text :
- https://doi.org/10.1038/s41598-024-54798-z