RT Journal Article
SR Electronic
T1 SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 743591
DO 10.1101/743591
A1 Meichen Dong
A1 Aatish Thennavan
A1 Eugene Urrutia
A1 Yun Li
A1 Charles M. Perou
A1 Fei Zou
A1 Yuchao Jiang
YR 2019
UL http://biorxiv.org/content/early/2019/08/22/743591.abstract
AB 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.