ArticleBriefings in bioinformatics2025
ProjectSVR: mapping single-cell RNA-seq data to reference atlases by supported vector regression.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Single-cell transcriptomic analysis deciphers heterogeneity and transcriptional regulatory programs of sepsis with different prognosis.Biology direct · 2026Article
- PCID2 is essential for spermatogonial differentiation by regulating alternative splicing.Cellular and molecular life sciences : CMLS · 2026Article
- Dynamic changes in histone lysine lactylation during meiosis prophase I in mouse spermatogenesis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- NAE1-mediated neddylation coordinates ubiquitination regulation of meiotic recombination during spermatogenesis.Theranostics · 2025Article
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- Comprehensive multi-omics analysis reveals the prognostic and immune regulatory characteristics of the PTPN family in osteosarcoma.PloS one · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
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Abstract
Mapping the query cells onto a well-constructed reference atlas, known as reference mapping, enables robust, reproducible interpretation of new single-cell RNA-seq data in the context of curated and annotated cell subtypes and states. However, existing methods often rely on complex integration frameworks or require re-access to raw data, limiting their applicability and reproducibility. To address this, we introduce ProjectSVR, a machine learning-based framework that formulates reference mapping as a multi-target regression task. By leveraging ensemble support vector regression (SVR) to learn the relationship between gene set activity scores and low-dimensional reference embeddings, ProjectSVR enables platform-agnostic and integration-independent mapping. Benchmarking across diverse biological contexts-including immune responses, developmental trajectories, and disease states-demonstrates that ProjectSVR achieves comparable accuracy and robustness to state-of-the-art methods, with reduced dependence on data-specific preprocessing. Our findings demonstrate that ProjectSVR is a valuable tool for reference mapping, considerably simplifying the analysis of scRNA-seq data when well-constructed reference atlases are available.
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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.