The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references
Open accessAbstract
Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.
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Cited by
Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
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Benchmarking of cell type deconvolution pipelines for transcriptomics data
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SpatialDWLS: accurate deconvolution of spatial transcriptomic data
Dong, R., & Yuan, G.-C. (2021) · Genome Biology · doi:10.1186/s13059-021-02362-7 · Cited by 304
Metadata
- Authors
- Dong, M., Thennavan, A., Urrutia, E., Li, Y., Perou, C. M., Zou, F., & Jiang, Y.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-01-10
- Volume
- 22
- Issue
- 1
- Pages
- 416-427
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W3000010184
- Cited by
- 252
- References
- 15
- FWCI
- 5.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
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