异质单细胞 RNA-seq 数据集的联合分析。
Joint analysis of heterogeneous single-cell RNA-seq dataset collections.
机构信息
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Biotech Research and Innovation Centre, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
出版信息
Nat Methods. 2019 Aug;16(8):695-698. doi: 10.1038/s41592-019-0466-z. Epub 2019 Jul 15.
Single-cell RNA sequencing is often applied in study designs that include multiple individuals, conditions or tissues. To identify recurrent cell subpopulations in such heterogeneous collections, we developed Conos, an approach that relies on multiple plausible inter-sample mappings to construct a global graph connecting all measured cells. The graph enables identification of recurrent cell clusters and propagation of information between datasets in multi-sample or atlas-scale collections.
单细胞 RNA 测序通常应用于包含多个个体、条件或组织的研究设计中。为了在这种异质的混合物中鉴定反复出现的细胞亚群，我们开发了 Conos 方法，该方法依赖于多个可能的样本间映射来构建一个连接所有测量细胞的全局图。该图能够识别反复出现的细胞簇，并在多样本或图谱规模的混合物中在数据集之间传播信息。
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