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HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses

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...

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Autores principales: Shi, Xiaoying, Yu, Zhiguang, Ren, Pengfei, Dong, Xin, Ding, Xuanxin, Song, Jiaming, Zhang, Jing, Li, Taiwen, Wang, Chenfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825509/
https://www.ncbi.nlm.nih.gov/pubmed/36318258
http://dx.doi.org/10.1093/nar/gkac1001
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author Shi, Xiaoying
Yu, Zhiguang
Ren, Pengfei
Dong, Xin
Ding, Xuanxin
Song, Jiaming
Zhang, Jing
Li, Taiwen
Wang, Chenfei
author_facet Shi, Xiaoying
Yu, Zhiguang
Ren, Pengfei
Dong, Xin
Ding, Xuanxin
Song, Jiaming
Zhang, Jing
Li, Taiwen
Wang, Chenfei
author_sort Shi, Xiaoying
collection PubMed
description 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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spelling pubmed-98255092023-01-10 HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses Shi, Xiaoying Yu, Zhiguang Ren, Pengfei Dong, Xin Ding, Xuanxin Song, Jiaming Zhang, Jing Li, Taiwen Wang, Chenfei Nucleic Acids Res Database Issue 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. Oxford University Press 2022-11-01 /pmc/articles/PMC9825509/ /pubmed/36318258 http://dx.doi.org/10.1093/nar/gkac1001 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Database Issue
Shi, Xiaoying
Yu, Zhiguang
Ren, Pengfei
Dong, Xin
Ding, Xuanxin
Song, Jiaming
Zhang, Jing
Li, Taiwen
Wang, Chenfei
HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses
title HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses
title_full HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses
title_fullStr HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses
title_full_unstemmed HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses
title_short HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses
title_sort husch: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses
topic Database Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825509/
https://www.ncbi.nlm.nih.gov/pubmed/36318258
http://dx.doi.org/10.1093/nar/gkac1001
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