ArticleNAR genomics and bioinformatics2023
GRACE: a comprehensive web-based platform for integrative single-cell transcriptome analysis.
Article in NAR genomics and bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- SC-framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets.iScience · 2026Article
- scHLens: a web server for hierarchically and interactively exploring single cell RNA-seq data.Briefings in bioinformatics · 2025Article
- Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.Genomics & informatics · 2025Review
- Investigating biological nitrogen fixation via single-cell transcriptomics.Journal of experimental botany · 2025Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large-scale single-cell RNA sequencing (scRNA-seq) has emerged as a robust method for dissecting cellular heterogeneity at single-cell resolution. However, to meet the increasingly high computational demands of non-programming experts, a user-friendly, scalable, and accessible online platform for analyzing scRNA-seq data is urgently needed. Here, we have developed a web-based platform GRACE (GRaphical Analyzing Cell Explorer) (http://grace.flowhub.com.cn or http://grace.jflab.ac.cn:28080) that enables online massive single-cell transcriptome analysis, improving interactivity and reproducibility using high-quality visualization frameworks. GRACE provides easy access to interactive visualization, customized parameters, and publication-quality graphs. Furthermore, it comprehensively integrates preprocessing, clustering, developmental trajectory inference, cell-cell communication, cell-type annotation, subcluster analysis, and pathway enrichment. In addition to the website platform, we also provide a Docker version that can be easily deployed on private servers. The source code for GRACE is freely available at (https://github.com/th00516/GRACE). Documentation and video tutorials are accessible from website homepage (http://grace.flowhub.com.cn). GRACE can analyze massive scRNA-seq data more flexibly and be accessible to the scientific community. This platform fulfills the major gap that exists between experimental (wet lab) and bioinformatic (dry lab) research.
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Registered trials
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.