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Related Experiment Video

Updated: Jul 7, 2025

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells
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SCInter: A comprehensive single-cell transcriptome integration database for human and mouse.

Jun Zhao1, Yuezhu Wang2, Chenchen Feng3

  • 1School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.

Computational and Structural Biotechnology Journal
|December 21, 2023
PubMed
Summary
This summary is machine-generated.

SCInter is a new database for single-cell RNA sequencing data, offering gene expression profiles for over 450 cell types. This resource aids in analyzing cell heterogeneity and developmental trajectories for immunology and oncology research.

Keywords:
Cell heterogeneityCell to cell communicationMulti-method automatic cell-type annotationSingle cell integration database

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cell Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity and developmental paths.
  • The rapid growth of scRNA-seq data necessitates efficient management for analysis and annotation.

Purpose of the Study:

  • To develop SCInter, a manually curated database for integrated human and mouse single-cell transcriptome data.
  • To provide a platform for comprehensive analysis and visualization of scRNA-seq datasets.

Main Methods:

  • Integrated 115 scRNA-seq datasets comprising 1016 samples across ~150 tissues/cell lines.
  • Developed automated pipelines for quality control, clustering, cell marker identification, and cell type annotation.
  • Implemented multi-method cell type annotation and cell differentiation trajectory prediction.

Main Results:

  • SCInter contains 8,016,646 cell markers for 457 identified cell types.
  • The database offers a user-friendly interface for querying, browsing, analyzing, and visualizing data.
  • Provides comprehensive QC reports and processing results for each dataset.

Conclusions:

  • SCInter facilitates the identification of cell types and subpopulations.
  • Enhances the study of cell heterogeneity, developmental trajectories, immunology, and oncology.
  • Supports effective annotation and analysis of large-scale single-cell data.