单细胞 RNA-Seq 的有效聚类后差异分析。
Valid Post-clustering Differential Analysis for Single-Cell RNA-Seq.
机构信息
Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
出版信息
Cell Syst. 2019 Oct 23;9(4):383-392.e6. doi: 10.1016/j.cels.2019.07.012. Epub 2019 Sep 11.
Abstract
Single-cell computational pipelines involve two critical steps: organizing cells (clustering) and identifying the markers driving this organization (differential expression analysis). State-of-the-art pipelines perform differential analysis after clustering on the same dataset. We observe that because clustering "forces" separation, reusing the same dataset generates artificially low p values and hence false discoveries. We introduce a valid post-clustering differential analysis framework, which corrects for this problem. We provide software at https://github.com/jessemzhang/tn_test.
摘要
单细胞计算管道包含两个关键步骤
组织细胞（聚类）和识别驱动这种组织的标记（差异表达分析）。最先进的管道在同一数据集上进行聚类后执行差异分析。我们观察到，由于聚类“强制”分离，因此重新使用相同的数据集生成人为的低 p 值和错误发现。我们引入了一个有效的聚类后差异分析框架，该框架纠正了这个问题。我们在 https://github.com/jessemzhang/tn_test 上提供了软件。
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