ArticleNature communications2020
Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers, 1 of them a synthesis that pooled it.
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Who cites it
94 citing papers in PubMed, 1 synthesis or guideline pooled it, 156 citations in OpenAlex.
- Interpretation of T cell states from single-cell transcriptomics data using reference atlases.Nature communications · 2021Pooled it
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- Explainable data generation from observed small samples by matrix tri-factorization.Nature communications · 2026Article
- DBP: Adaptive and Interpretable Factor Analysis for Single-cell RNA-seq Data with Deep Beta Processes.Genomics, proteomics & bioinformatics · 2026Article
- scDifformer: diffusion-based post-training for virtual cell modeling across large-scale single-cell data.Nucleic acids research · 2026Article
- Computational modelling of cell identity.The Biochemical journal · 2026Review
- MultiSP deciphers tissue structure and multicellular communication from spatial multi-omics data.Cell genomics · 2026Article
- Cell neighborhood topology directs rare cell population identification.Nature communications · 2026Article
- STEVE: Single-cell Transcriptomics Expression Visualization and Evaluation.bioRxiv : the preprint server for biology · 2026Article
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- Multi-species integration, alignment and annotation of single-cell RNA-seq data with CAMEX.Nature communications · 2026Article
- scLong: a billion-parameter foundation model for capturing long-range gene context in single-cell transcriptomics.Nature communications · 2026Article
- CELLama: Foundation Model for Single Cell and Spatial Transcriptomics by Cell Embedding Leveraging Language Model Abilities.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- GAHIB: graph attention VAE with a hyperbolic information bottleneck for biologically structured single-cell representations.Frontiers in genetics · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Integrated single cell transcriptomics analysis for unraveling heterogeneity and plasticity of root cells for sustainable and regenerative agriculture.Frontiers in plant science · 2026Review
- LAIOR: a hyperbolic neural ODE variational framework for interpretable single-cell manifold learning and trajectory inference.Frontiers in genetics · 2026Article
- scSpecies: enhancement of network architecture alignment in comparative single-cell studies.Genome biology · 2025Article
- Cell Decoder: decoding cell identity with multi-scale explainable deep learning.Genome biology · 2025Article
- Evaluation of cell type annotation reliability using a large language model-based identifier.Communications biology · 2025Article
34 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA-seq (scRNA-seq) is being used widely to resolve cellular heterogeneity. With the rapid accumulation of public scRNA-seq data, an effective and efficient cell-querying method is critical for the utilization of the existing annotations to curate newly sequenced cells. Such a querying method should be based on an accurate cell-to-cell similarity measure, and capable of handling batch effects properly. Herein, we present Cell BLAST, an accurate and robust cell-querying method built on a neural network-based generative model and a customized cell-to-cell similarity metric. Through extensive benchmarks and case studies, we demonstrate the effectiveness of Cell BLAST in annotating discrete cell types and continuous cell differentiation potential, as well as identifying novel cell types. Powered by a well-curated reference database and a user-friendly Web server, Cell BLAST provides the one-stop solution for real-world scRNA-seq cell querying and annotation.
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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.