期刊
BRIEFINGS IN BIOINFORMATICS
卷 22, 期 1, 页码 416-427出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbz166
关键词
single-cell RNA sequencing; bulk RNA sequencing; gene expression deconvolution; ENSEMBLE; batch effect
资金
- National Institutes of Health [T32 ES007018, R01 HL129132, R01 GM105785, P30 ES010126, P01 CA142538, R35 GM118102, UL1 TR002489]
- National Cancer Institute Breast SPORE [P50 CA5822, R01 CA148761]
- Breast Cancer Research Foundation
- UNC Lineberger Comprehensive Cancer Center [2017T109]
- UNC Computational Medicine Program
SCDC is a deconvolution method for bulk RNA-seq data that improves the accuracy of cell-type decomposition. It leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets and integrates deconvolution results from different experiments and laboratories. The study demonstrates that SCDC outperforms existing methods in both settings.
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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