Decision level integration of unimodal and multimodal single cell data with scTriangulate.
Decision level integration of unimodal and multimodal single cell data with scTriangulate.
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DOI：
10.1038/s41467-023-36016-y
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发表时间：
2023-01-25
影响因子：
16.6
通讯作者：
Salomonis, Nathan
中科院分区：
文献类型：
--
作者：
Li, Guangyuan;Song, Baobao;Singh, Harinder;Surya Prasath, V. B.;Leighton Grimes, H.;Salomonis, Nathan
从单一和多模式单细胞数据集中决定性地描绘细胞身份是复杂的，因为不同的模式，聚类方法和参考图谱。我们描述了scTriangulate，一个计算框架，用于混合和匹配多个聚类结果，模态，相关算法和分辨率，以实现最佳解决方案。与选择“共识”的集成方法不同，scTriangulate通过联盟迭代选择最稳定的解决方案。当对不同的多模态技术进行评估时，scTriangulate在识别高置信度细胞群和模态特定亚型方面优于其他方法。与依赖于特定模态的联合嵌入或几何图的现有集成策略不同，scTriangulate对原始基础值的分布没有任何假设。因此，这种方法可以解决前所未有的整合挑战，包括自动化参考细胞图谱构建，解析分子定义的细胞群内的克隆结构和细分簇以发现剪接定义的疾病亚型的能力。scTriangulate是一种灵活的策略，用于统一集成来自几乎无限来源的单细胞或多模式聚类解决方案。单细胞基因组学已经扩展到测量同一细胞内的不同分子模式。在这里，作者提供了一个名为scTriangulate的计算框架，用于整合来自不同独立来源、算法和模式的聚类注释，以定义统计稳定的群体。
Decisively delineating cell identities from uni- and multimodal single-cell datasets is complicated by diverse modalities, clustering methods, and reference atlases. We describe scTriangulate, a computational framework to mix-and-match multiple clustering results, modalities, associated algorithms, and resolutions to achieve an optimal solution. Rather than ensemble approaches which select the “consensus”, scTriangulate picks the most stable solution through coalitional iteration. When evaluated on diverse multimodal technologies, scTriangulate outperforms alternative approaches to identify high-confidence cell-populations and modality-specific subtypes. Unlike existing integration strategies that rely on modality-specific joint embedding or geometric graphs, scTriangulate makes no assumption about the distributions of raw underlying values. As a result, this approach can solve unprecedented integration challenges, including the ability to automate reference cell-atlas construction, resolve clonal architecture within molecularly defined cell-populations and subdivide clusters to discover splicing-defined disease subtypes. scTriangulate is a flexible strategy for unified integration of single-cell or multimodal clustering solutions, from nearly unlimited sources. Single-cell genomics has expanded to measure diverse molecular modalities within the same cell. Here the authors provide a computational framework called scTriangulate to integrate cluster annotations from diverse independent sources, algorithms, and modalities to define statistically stable populations.
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影响因子：
48
作者：
Biancalani T;Scalia G;Buffoni L;Avasthi R;Lu Z;Sanger A;Tokcan N;Vanderburg CR;Segerstolpe Å;Zhang M;Avraham-Davidi I;Vickovic S;Nitzan M;Ma S;Subramanian A;Lipinski M;Buenrostro J;Brown NB;Fanelli D;Zhuang X;Macosko EZ;Regev A
通讯作者：
Regev A
影响因子：
32.4
作者：
Nakahira, Masakiyo;Tanaka, Takashi;Grusby, Michael J.
通讯作者：
Grusby, Michael J.
影响因子：
2.6
作者：
Hay SB;Ferchen K;Chetal K;Grimes HL;Salomonis N
通讯作者：
Salomonis N
影响因子：
32.4
作者：
Guilliams M;Dutertre CA;Scott CL;McGovern N;Sichien D;Chakarov S;Van Gassen S;Chen J;Poidinger M;De Prijck S;Tavernier SJ;Low I;Irac SE;Mattar CN;Sumatoh HR;Low GHL;Chung TJK;Chan DKH;Tan KK;Hon TLK;Fossum E;Bogen B;Choolani M;Chan JKY;Larbi A;Luche H;Henri S;Saeys Y;Newell EW;Lambrecht BN;Malissen B;Ginhoux F
通讯作者：
Ginhoux F
影响因子：
5.5
作者：
Ahn J;Heo S;Lee J;Bang D
通讯作者：
Bang D