ScLineageAtlas: a comprehensive single-cell genomics database for characterizing cellular clones in cancer

Accurate identification of clonal relationships between cell populations is crucial for investigating cellular differentiation trajectories and gaining insights into the underlying mechanisms of cancer initiation and development. The Single Cell Lineage Atlas (ScLineageAtlas; https://www.scladb.geneis.org.cn) is a comprehensive single-cell genomics database that characterizes cellular clones across various cancer types. The database currently includes 24 processed single-cell RNA sequencing datasets spanning 13 different cancer types. ScLineageAtlas leverages advanced computational methods to identify cellular clones, providing researchers with a detailed understanding of clone relationships and evolutionary dynamics. Additionally, the database offers comprehensive metadata for each sample, enabling researchers to explore contextual information and sample characteristics. The spatial visualization of cell clones presented in the ScLineageAtlas provides a valuable tool for enhancing our understanding of the genetic heterogeneity within the tumour microenvironment. Through the analysis of biological differences between these diverse cell populations, researchers can explore key genes and signalling pathways associated with cancer initiation, development, and therapeutic efficacy. In summary, the ScLineageAtlas serves as a user-friendly platform for data operations on cellular clones, facilitating the understanding of tumour heterogeneity, differentiation trajectories, and evolution. It thus contributes significantly to cancer research and clinical practice.

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Database
Published
2025-01-01
DOI
https://doi.org/10.1093/database/baaf046
Primary Topic
Single-cell and spatial transcriptomics
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article
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article

ScLineageAtlas: a comprehensive single-cell genomics database for characterizing cellular clones in cancer

Haotian Tian, Jialiang Yang, Geng Tian, Yankun Liu et al.
Database
Single-cell and spatial transcriptomics
article

ScLineageAtlas: a comprehensive single-cell genomics database for characterizing cellular clones in cancer

Haotian Tian, Jialiang Yang, Geng Tian, Yankun Liu, Junlin Xu, Meijun Zhang, Tingting Hui, Rui Hou, Jinyang Liu
article en

Abstract

Accurate identification of clonal relationships between cell populations is crucial for investigating cellular differentiation trajectories and gaining insights into the underlying mechanisms of cancer initiation and development. The Single Cell Lineage Atlas (ScLineageAtlas; https://www.scladb.geneis.org.cn) is a comprehensive single-cell genomics database that characterizes cellular clones across various cancer types. The database currently includes 24 processed single-cell RNA sequencing datasets spanning 13 different cancer types. ScLineageAtlas leverages advanced computational methods to identify cellular clones, providing researchers with a detailed understanding of clone relationships and evolutionary dynamics. Additionally, the database offers comprehensive metadata for each sample, enabling researchers to explore contextual information and sample characteristics. The spatial visualization of cell clones presented in the ScLineageAtlas provides a valuable tool for enhancing our understanding of the genetic heterogeneity within the tumour microenvironment. Through the analysis of biological differences between these diverse cell populations, researchers can explore key genes and signalling pathways associated with cancer initiation, development, and therapeutic efficacy. In summary, the ScLineageAtlas serves as a user-friendly platform for data operations on cellular clones, facilitating the understanding of tumour heterogeneity, differentiation trajectories, and evolution. It thus contributes significantly to cancer research and clinical practice.

DatabaseVol. 2025
Hunan University (CN), Tangshan People's Hospital (CN), Qingdao Institute of Marine Geology (CN), Petroleum Technology Company (Norway) (NO), Beijing Chaoyang Emergency Medical Center (CN), South University (US)
Openalex Percentile: Top 77%
Single-cell and spatial transcriptomics
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