SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

• Published in: Briefings in bioinformatics Vol. 22; no. 1; pp. 416 - 427
• Main Authors: Dong, Meichen, Thennavan, Aatish, Urrutia, Eugene, Li, Yun, Perou, Charles M, Zou, Fei, Jiang, Yuchao
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 18.01.2021; Oxford Publishing Limited (England)
• Subjects: Animals; Cell lines; Datasets; Deconvolution; Female; Gene expression; Gene Expression Regulation, Neoplastic; Gene sequencing; Humans; Islets of Langerhans - metabolism; Mammary gland; Mammary glands; Mammary Glands, Animal - metabolism; MCF-7 Cells; Mice; Pancreas; Phenotypes; Reference Standards; Ribonucleic acid; RNA; RNA-Seq - methods; RNA-Seq - standards; Single-Cell Analysis - methods; Single-Cell Analysis - standards; Software - standards; Transcriptomics; single-cell RNA sequencing; ENSEMBLE; batch effect; bulk RNA sequencing; gene expression deconvolution
• DOI: 10.1093/bib/bbz166
• ISSN: 1477-4054; 1467-5463

Abstract 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.