Classification of low quality cells from single-cell RNA-seq data.
Classification of low quality cells from single-cell RNA-seq data.
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DOI：
10.1186/s13059-016-0888-1
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发表时间：
2016-02-17
期刊：
影响因子：
12.3
通讯作者：
Teichmann SA
中科院分区：
文献类型：
--
作者：
Ilicic T;Kim JK;Kolodziejczyk AA;Bagger FO;McCarthy DJ;Marioni JC;Teichmann SA
单细胞RNA测序（scRNA-seq）在生物医学研究中具有广泛的应用。关键挑战之一是确保下游分析中仅包括单个活细胞，因为包含受损细胞不可避免地会影响数据解释。在这里，我们提出了一种处理scRNA-seq数据和检测低质量细胞的通用方法，使用了一组超过20种生物和技术特征。与传统方法相比，我们的方法在超过5，000个细胞（包括CD 4 + T细胞，骨髓树突状细胞和小鼠胚胎干细胞）上进行测试时，分类准确率提高了30%以上。本文的在线版本（doi：10.1186/s13059-016-0888-1）包含补充材料，可供授权用户使用。
Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells. The online version of this article (doi:10.1186/s13059-016-0888-1) contains supplementary material, which is available to authorized users.