Accurate estimation of cell-type composition from gene expression data
Accurate estimation of cell-type composition from gene expression data
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DOI：
10.1038/s41467-019-10802-z
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发表时间：
2019-07-05
影响因子：
16.6
通讯作者：
Yuan, Guo-Cheng
中科院分区：
文献类型：
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作者：
Tsoucas, Daphne;Dong, Rui;Yuan, Guo-Cheng
单细胞转录组学技术的快速发展有助于揭示细胞群体内的细胞异质性。然而，由于技术简单和成本低，大量RNA-seq仍然是定量基因表达水平的主要工具。鉴于从单细胞方法中获得的新知识，为了最有效地从大量数据中提取信息，我们开发了一种新的算法来估计单细胞rna -seq衍生的细胞类型特征中大量数据的细胞类型组成。与使用各种真实RNA-seq数据集的现有方法的比较表明，我们的新方法比以前的方法更准确和全面，特别是对于稀有细胞类型的估计。更重要的是，我们的方法可以检测细胞类型组成响应外部扰动的变化，从而为剖析药物治疗或条件变化对细胞类型的特异性影响提供了一种有价值的、具有成本效益的方法。因此，我们的方法适用于广泛的生物学和临床研究。
The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly, our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations.