ArticleNucleic acids research2023
HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- CanInternational journal of molecular sciences · 2026Article
- hECA v2.0: an AI-ready ensemble cell atlas of single-cell RNA and ATAC sequencing data.Scientific data · 2025Article
- Evaluation of T Cell Receptor Construction Methods from scRNA-Seq Data.Genomics, proteomics & bioinformatics · 2025Article
- Article
- scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration.Briefings in bioinformatics · 2024Article
- TargetGene: a comprehensive database of cell-type-specific target genes for genetic variants.Nucleic acids research · 2024Article
- ZEBRA: a hierarchically integrated gene expression atlas of the murine and human brain at single-cell resolution.Nucleic acids research · 2024Article
- Monocytes subsets altered distribution and dysregulated plasma hsa-miR-21-5p and hsa-miR-155-5p in HCV-linked liver cirrhosis progression to hepatocellular carcinoma.Journal of cancer research and clinical oncology · 2023Article
- Single-cell assignment using multiple-adversarial domain adaptation network with large-scale references.Cell reports methods · 2023Article
- Spatial transcriptomics: recent developments and insights in respiratory research.Military Medical Research · 2023Review
- The 2023 Nucleic Acids Research Database Issue and the online molecular biology database collection.Nucleic acids research · 2023Article
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding gene expression patterns across different human cell types is crucial for investigating mechanisms of cell type differentiation, disease occurrence and progression. The recent development of single-cell RNA-seq (scRNA-seq) technologies significantly boosted the characterization of cell type heterogeneities in different human tissues. However, the huge number of datasets in the public domain also posed challenges in data integration and reuse. We present Human Universal Single Cell Hub (HUSCH, http://husch.comp-genomics.org), an atlas-scale curated database that integrates single-cell transcriptomic profiles of nearly 3 million cells from 185 high-quality human scRNA-seq datasets from 45 different tissues. All the data in HUSCH were uniformly processed and annotated with a standard workflow. In the single dataset module, HUSCH provides interactive gene expression visualization, differentially expressed genes, functional analyses, transcription regulators and cell-cell interaction analyses for each cell type cluster. Besides, HUSCH integrated different datasets in the single tissue module and performs data integration, batch correction, and cell type harmonization. This allows a comprehensive visualization and analysis of gene expression within each tissue based on single-cell datasets from multiple sources and platforms. HUSCH is a flexible and comprehensive data portal that enables searching, visualizing, analyzing, and downloading single-cell gene expression for the human tissue atlas.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.