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

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...