Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census.
Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census.
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DOI：
10.1093/bioinformatics/btad714
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发表时间：
2023-12-01
期刊：
影响因子：
5.8
通讯作者：
De, Subhajyoti
中科院分区：
文献类型：
--
作者：
Ghaddar, Bassel;De, Subhajyoti
细胞类型注释是单细胞RNA序列数据分析中耗时但关键的第一步，特别是当存在多个具有重叠标记基因的相似细胞亚型时。现有的自动标注方法有许多局限性，包括需要大的参考数据集、高计算时间、浅标注分辨率以及难以识别癌细胞或其最可能的起源细胞。我们开发了人口普查，这是一种用于单细胞RNA-SEQ数据的生物学直观和全自动的细胞类型识别方法，可以深入注释哺乳动物组织中的正常细胞，并识别恶性细胞及其可能的起源细胞。在细胞分化的内在分层发展计划的推动下，人口普查推断出分层的细胞类型关系，并使用梯度增强的决策树来利用节点细胞类型关系来实现高预测速度和准确性。当以44个地图集规模的正常组织、癌症组织、人类组织和老鼠组织为基准时，人口普查在多个指标上显著优于最先进的方法，并自然地预测不同癌症的起源细胞。在Tabula Sapiens上进行了预培训，以对来自24个器官的175种细胞类型进行分类；然而，用户可以无缝地培训他们自己的模型，以用于定制应用。人口普查可在泽诺多https://zenodo.org/records/7017103和我们的Github https://github.com/sjdlabgroup/Census.上获得
Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods have a number of limitations, including requiring large reference datasets, high computation time, shallow annotation resolution, and difficulty in identifying cancer cells or their most likely cell of origin. We developed Census, a biologically intuitive and fully automated cell-type identification method for single-cell RNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. Motivated by the inherently stratified developmental programs of cellular differentiation, Census infers hierarchical cell-type relationships and uses gradient-boosted \decision trees that capitalize on nodal cell-type relationships to achieve high prediction speed and accuracy. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly outperforms state-of-the-art methods across multiple metrics and naturally predicts the cell-of-origin of different cancers. Census is pretrained on the Tabula Sapiens to classify 175 cell-types from 24 organs; however, users can seamlessly train their own models for customized applications. Census is available at Zenodo https://zenodo.org/records/7017103 and on our Github https://github.com/sjdlabgroup/Census.
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DOI：
10.1126/science.abl5197
发表时间：
2022-05-13
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Domínguez Conde C;Xu C;Jarvis LB;Rainbow DB;Wells SB;Gomes T;Howlett SK;Suchanek O;Polanski K;King HW;Mamanova L;Huang N;Szabo PA;Richardson L;Bolt L;Fasouli ES;Mahbubani KT;Prete M;Tuck L;Richoz N;Tuong ZK;Campos L;Mousa HS;Needham EJ;Pritchard S;Li T;Elmentaite R;Park J;Rahmani E;Chen D;Menon DK;Bayraktar OA;James LK;Meyer KB;Yosef N;Clatworthy MR;Sims PA;Farber DL;Saeb-Parsy K;Jones JL;Teichmann SA
通讯作者：
Teichmann SA
人肝肿瘤微环境的单个细胞地图集。
影响因子：
9.9
作者：
Massalha H;Bahar Halpern K;Abu-Gazala S;Jana T;Massasa EE;Moor AE;Buchauer L;Rozenberg M;Pikarsky E;Amit I;Zamir G;Itzkovitz S
通讯作者：
Itzkovitz S
影响因子：
64.8
作者：
Litviňuková M;Talavera-López C;Maatz H;Reichart D;Worth CL;Lindberg EL;Kanda M;Polanski K;Heinig M;Lee M;Nadelmann ER;Roberts K;Tuck L;Fasouli ES;DeLaughter DM;McDonough B;Wakimoto H;Gorham JM;Samari S;Mahbubani KT;Saeb-Parsy K;Patone G;Boyle JJ;Zhang H;Zhang H;Viveiros A;Oudit GY;Bayraktar OA;Seidman JG;Seidman CE;Noseda M;Hubner N;Teichmann SA
通讯作者：
Teichmann SA
影响因子：
6
作者：
Pasquini G;Rojo Arias JE;Schäfer P;Busskamp V
通讯作者：
Busskamp V
影响因子：
12.3
作者：
Abdelaal, Tamim;Michielsen, Lieke;Mahfouz, Ahmed
通讯作者：
Mahfouz, Ahmed