BCseq: accurate single cell RNA-seq quantification with bias correction.
BCseq: accurate single cell RNA-seq quantification with bias correction.
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DOI：
10.1093/nar/gky308
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发表时间：
2018-08-21
影响因子：
14.9
通讯作者：
Zheng S
中科院分区：
文献类型：
--
作者：
Chen L;Zheng S
随着技术的快速进步，单细胞RNA-seq（scRNA-seq）已被用于检测表现出不同基因表达谱的细胞亚型，并追踪发育和疾病中的细胞转变。然而，scRNA-seq用于新发现的潜力受到后续数据分析的鲁棒性的限制。在这里，我们提出了一个强大的模型，BCseq（偏差校正测序分析），以准确地量化scRNA-seq的基因表达。BCseq以数据自适应的方式纠正scRNA-seq的固有偏差，并有效消除技术噪音。BCseq通过对相似细胞的加权考虑来挽救辍学者。具有较高测序深度的细胞对非线性定量贡献更大。此外，BCseq为每个细胞中每个基因的表达分配质量分数，为用户提供了一个客观的衡量标准来选择基因进行下游分析。与现有的scRNA-seq方法相比，BCseq在检测差异表达（DE）基因和细胞亚型分类方面表现出更高的鲁棒性。
With rapid technical advances, single cell RNA-seq (scRNA-seq) has been used to detect cell subtypes exhibiting distinct gene expression profiles and to trace cell transitions in development and disease. However, the potential of scRNA-seq for new discoveries is constrained by the robustness of subsequent data analysis. Here we propose a robust model, BCseq (bias-corrected sequencing analysis), to accurately quantify gene expression from scRNA-seq. BCseq corrects inherent bias of scRNA-seq in a data-adaptive manner and effectively removes technical noise. BCseq rescues dropouts through weighted consideration of similar cells. Cells with higher sequencing depths contribute more to the quantification nonlinearly. Furthermore, BCseq assigns a quality score for the expression of each gene in each cell, providing users an objective measure to select genes for downstream analysis. In comparison to existing scRNA-seq methods, BCseq demonstrates increased robustness in detection of differentially expressed (DE) genes and cell subtype classification.
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