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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.<\/jats:p>","DOI":"10.1093\/bib\/bbz166","type":"journal-article","created":{"date-parts":[[2019,12,3]],"date-time":"2019-12-03T15:13:00Z","timestamp":1575385980000},"page":"416-427","source":"Crossref","is-referenced-by-count":230,"title":["SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing 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