Efficient and precise single-cell reference atlas mapping with Symphony.
Efficient and precise single-cell reference atlas mapping with Symphony.
复制标题
使用Symphony进行高效、精确的单细胞参考图谱绘制。
DOI：
10.1038/s41467-021-25957-x
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发表时间：
2021-10-07
影响因子：
16.6
通讯作者：
Raychaudhuri S
中科院分区：
文献类型：
--
作者：
Kang JB;Nathan A;Weinand K;Zhang F;Millard N;Rumker L;Moody DB;Korsunsky I;Raychaudhuri S
单细胞技术和整合算法的最新进展使得构建涵盖许多供体、研究、疾病状态和测序平台的综合参考图谱成为可能。就像将测序读数映射到参考基因组一样，必须能够将查询细胞映射到复杂的数百万细胞参考图谱上，以快速识别相关细胞状态和表型。我们提出了Symphony（https：//github.com/immunogenomics/symphony），这是一种用于以方便，可移植的格式构建大规模集成参考图谱的算法，可以在几秒钟内实现高效的查询映射。Symphony将查询单元定位在稳定的低维引用嵌入中，便于将引用定义的注释复制到查询的下游传输。我们在多个真实世界的数据集中展示了Symphony的强大功能，包括（1）映射多供体，多物种查询以预测胰腺细胞类型，（2）沿着胎儿肝脏造血的发育轨迹定位查询细胞，以及（3）使用记忆T细胞的多模式CITE-seq图谱推断表面蛋白表达。生成的单细胞RNA-seq数据集的数量正在迅速增加，使得将细胞类型映射到精心策划的参考文献的方法变得越来越重要。在这里，作者提出了一种精确的方法，可以在几秒钟内将单个细胞映射到参考图谱上。
Recent advances in single-cell technologies and integration algorithms make it possible to construct comprehensive reference atlases encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a reference genome, it is essential to be able to map query cells onto complex, multimillion-cell reference atlases to rapidly identify relevant cell states and phenotypes. We present Symphony (https://github.com/immunogenomics/symphony), an algorithm for building large-scale, integrated reference atlases in a convenient, portable format that enables efficient query mapping within seconds. Symphony localizes query cells within a stable low-dimensional reference embedding, facilitating reproducible downstream transfer of reference-defined annotations to the query. We demonstrate the power of Symphony in multiple real-world datasets, including (1) mapping a multi-donor, multi-species query to predict pancreatic cell types, (2) localizing query cells along a developmental trajectory of fetal liver hematopoiesis, and (3) inferring surface protein expression with a multimodal CITE-seq atlas of memory T cells. The number of single-cell RNA-seq datasets generated is increasing rapidly, making methods that map cell types to well-curated references increasingly important. Here, the authors propose an accurate method for mapping single cells onto a reference atlas in seconds.