ArticleiMeta2025
Comprehensive analysis of multi-omics single-cell data using the single-cell analyst.
Article in iMeta, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Article
- A large-scale single-cell transcriptomic atlas indicates the immune panorama of influenza A infection.iMeta · 2026Article
- Comparative Single-Cell Transcriptomic Atlas Reveals the Genetic Regulation of Reproductive Traits.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Editorial: Artificial intelligence in multi-omics: advancing tumor metastasis prediction and mechanism analysis.Frontiers in cell and developmental biology · 2026Article
- Single-cell analysis identifies a VIM+ glioma stem-like vascular-immune state linked to myeloid OSM signaling and CEBPD activity.Frontiers in immunology · 2026Article
- Protocol for decoding immune predictors of response to immunotherapy through pan-cancer multiomics analysis.STAR protocols · 2025Article
- Omics landscapes in molecular mechanisms withFood chemistry. Molecular sciences · 2025Review
- Multi-omics profiling reveals downregulated tumor lysine metabolism reshaping the immune microenvironment and therapeutic responses in hepatocellular carcinoma.Journal of translational medicine · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid advancement of multi-omics single-cell technologies has significantly enhanced our ability to investigate complex biological systems at unprecedented resolution. However, many existing analysis tools are complex, requiring substantial coding expertize, which can be a barrier for computationally less competent researchers. To address this challenge, we present single-cell analyst, a user-friendly, web-based platform to facilitate comprehensive multi-omics analysis. Single-cell analyst supports a wide range of data types, including six single-cell omics: single-cell RNA sequencing (scRNA-sequencing), single-cell assay for transposase accessible chromatin sequencing (scATAC-seq sequencing), single-cell immune profiling (scImmune profiling), single-cell copy number variation, cytometry by time-of-flight, and flow cytometry and spatial transcriptomics, and enables researchers to perform integrated analyses without requiring programming skills. The platform offers both online and offline modes, providing flexibility for various use cases. It automates critical analysis steps, such as quality control, data processing, and phenotype-specific analyses, while also offering interactive, publication-ready visualizations. With over 20 interactive tools for intermediate analysis, single cell analyst simplifies workflows and significantly reduces the learning curve typically associated with similar platforms. This robust tool accommodates datasets of varying sizes, completing analyses within minutes to hours depending on the data volume, and ensures efficient use of computational resources. By democratizing the complex process of multi-omics analysis, single-cell analyst serves as an accessible, all-encompassing solution for researchers of diverse technical backgrounds. The platform is freely accessible at www.singlecellanalyst.org.
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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.