SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references. Issue 1 (10th January 2020)

• Record Type: Journal Article
• Authors: Dong, Meichen; Thennavan, Aatish; Urrutia, Eugene; Li, Yun; Perou, Charles M; Zou, Fei; Jiang, Yuchao
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 1(2021)
• Page Start: 416
• Page End: 427
• Publication Date: 2020-01-10
• Subjects: single-cell RNA sequencing -- bulk RNA sequencing -- gene expression deconvolution -- ENSEMBLE -- batch effect; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbz166
• Languages: English
• ISSNs: 1467-5463

Abstract

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.