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

Authors: Dong, Meichen; Thennavan, Aatish; Urrutia, Eugene; Li, Yun; Perou, Charles M; Zou, Fei; Jiang, Yuchao

Published In Brief Bioinform, (2021 Jan 18)

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.

PubMed ID: 31925417 Exiting the NIEHS site

MeSH Terms: Animals; Female; Gene Expression Regulation, Neoplastic; Humans; Islets of Langerhans/metabolism; MCF-7 Cells; Mammary Glands, Animal/metabolism; Mice; RNA-Seq/methods*; RNA-Seq/standards; Reference Standards; Single-Cell Analysis/methods*; Single-Cell Analysis/standards; Software/standards*

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