Article ; Online: Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM.
2023 Volume 24, Issue 1, Page(s) 349
Abstract: Background: Quantifying cell-type abundance in bulk tissue RNA-sequencing enables researchers to better understand complex systems. Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell RNA-sequencing (scRNA- ...
| Abstract | Background: Quantifying cell-type abundance in bulk tissue RNA-sequencing enables researchers to better understand complex systems. Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell RNA-sequencing (scRNA-seq) data to make these calculations. Single-nuclei RNA-sequencing (snRNA-seq) reference data can be used instead of scRNA-seq data for tissues such as human brain where single-cell data are difficult to obtain, but accuracy suffers due to sequencing differences between the technologies. Results: We propose a modification to MuSiC entitled 'DeTREM' which compensates for sequencing differences between the cell-type signature and bulk RNA-seq datasets in order to better predict cell-type fractions. We show DeTREM to be more accurate than MuSiC in simulated and real human brain bulk RNA-sequencing datasets with various cell-type abundance estimates. We also compare DeTREM to SCDC and CIBERSORTx, two recent deconvolution methods that use scRNA-seq cell-type signatures. We find that they perform well in simulated data but produce less accurate results than DeTREM when used to deconvolute human brain data. Conclusion: DeTREM improves the deconvolution accuracy of MuSiC and outperforms other deconvolution methods when applied to snRNA-seq data. DeTREM enables accurate cell-type deconvolution in situations where scRNA-seq data are not available. This modification improves characterization cell-type specific effects in brain tissue and identification of cell-type abundance differences under various conditions. |
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| MeSH term(s) | Humans ; RNA/genetics ; Brain ; RNA, Small Nuclear ; RNA-Seq ; Base Sequence |
| Chemical Substances | RNA (63231-63-0) ; RNA, Small Nuclear |
| Language | English |
| Publishing date | 2023-09-19 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 2041484-5 |
| ISSN | 1471-2105 ; 1471-2105 |
| ISSN (online) | 1471-2105 |
| ISSN | 1471-2105 |
| DOI | 10.1186/s12859-023-05476-w |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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