SCInter: A comprehensive single-cell transcriptome integration database for human and mouse

Jun Zhao; Yuezhu Wang; Chenchen Feng; Mingxue Yin; Yu Gao; Ling Wei; Chao Song; Bo Ai; Qiuyu Wang; Jian Zhang; Jiang Zhu; Chunquan Li

doi:10.1016/j.csbj.2023.11.024

SCInter: A comprehensive single-cell transcriptome integration database for human and mouse

Comput Struct Biotechnol J. 2023 Nov 15:23:77-86. doi: 10.1016/j.csbj.2023.11.024. eCollection 2024 Dec.

Authors

Jun Zhao¹, Yuezhu Wang², Chenchen Feng³, Mingxue Yin^{4

5

6

7}, Yu Gao¹, Ling Wei^{8

9}, Chao Song^{4

3

5}, Bo Ai¹, Qiuyu Wang^{4

3

5

6

7}, Jian Zhang¹, Jiang Zhu¹, Chunquan Li^{4

3

5

6

7

10}

Affiliations

¹ School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
² School of Artificial Intelligence, Jilin University, Changchun 130012, China.
³ School of Computer, University of South China, Hengyang, Hunan 421001, China.
⁴ The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
⁵ Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China.
⁶ Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
⁷ Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
⁸ Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing 100191, China.
⁹ Cancer Center, Peking University Third Hospital, Beijing 100191, China.
¹⁰ National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.

Abstract

Single-cell RNA sequencing (scRNA-seq), which profiles gene expression at the cellular level, has effectively explored cell heterogeneity and reconstructed developmental trajectories. With the increasing research on diseases and biological processes, scRNA-seq datasets are accumulating rapidly, highlighting the urgent need for collecting and processing these data to support comprehensive and effective annotation and analysis. Here, we have developed a comprehensive Single-Cell transcriptome integration database for human and mouse (SCInter, https://bio.liclab.net/SCInter/index.php), which aims to provide a manually curated database that supports the provision of gene expression profiles across various cell types at the sample level. The current version of SCInter includes 115 integrated datasets and 1016 samples, covering nearly 150 tissues/cell lines. It contains 8016,646 cell markers in 457 identified cell types. SCInter enabled comprehensive analysis of cataloged single-cell data encompassing quality control (QC), clustering, cell markers, multi-method cell type automatic annotation, predicting cell differentiation trajectories and so on. At the same time, SCInter provided a user-friendly interface to query, browse, analyze and visualize each integrated dataset and single cell sample, along with comprehensive QC reports and processing results. It will facilitate the identification of cell type in different cell subpopulations and explore developmental trajectories, enhancing the study of cell heterogeneity in the fields of immunology and oncology.

Keywords: Cell heterogeneity; Cell to cell communication; Multi-method automatic cell-type annotation; Single cell integration database.