ArticleNAR genomics and bioinformatics2023
Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data.
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 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
19 citing papers in PubMed.
- A unified network systems approach uncovers a core program underlying T follicular helper cell differentiation.Nature communications · 2026Article
- The Cell Ontology in the age of single-cell omics.Scientific data · 2026Article
- scGeneBank: cross-species screening of functional gene sets at single-cell resolution.Nucleic acids research · 2026Article
- Electroconvulsive Therapy Induces Dopaminergic Axon Regeneration, Corticostriatal Remodeling, and Restoration of Motor Function in Parkinsonian Mice.bioRxiv : the preprint server for biology · 2025Article
- Inferring pathway activity from single-cell and spatial transcriptomics data with PaaSc.PLoS computational biology · 2025Article
- Single-cell multi-omics in cancer immunotherapy: from tumor heterogeneity to personalized precision treatment.Molecular cancer · 2025Review
- Pathway Analysis Interpretation in the Multi-Omic Era.Biotech (Basel (Switzerland)) · 2025Review
- Molecular landscape of tumor-associated tissue-resident memory T cells in tumor microenvironment of hepatocellular carcinoma.Cell communication and signaling : CCS · 2025Article
- Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.Nature reviews. Molecular cell biology · 2025Review
- B-Lightning: using bait genes for marker gene hunting in single-cell data with complex heterogeneity.Briefings in bioinformatics · 2024Article
- Comparative analysis of single-cell pathway scoring methods and a novel approach.NAR genomics and bioinformatics · 2024Article
- DeepGSEA: explainable deep gene set enrichment analysis for single-cell transcriptomic data.Bioinformatics (Oxford, England) · 2024Article
- irGSEA: the integration of single-cell rank-based gene set enrichment analysis.Briefings in bioinformatics · 2024Article
- Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer.Genome medicine · 2024Article
- scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration.Briefings in bioinformatics · 2024Article
- Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity.Nature communications · 2023Article
- Predicting drug response from single-cell expression profiles of tumours.BMC medicine · 2023Article
- vissE.cloud: a webserver to visualise higher order molecular phenotypes from enrichment analysis.Nucleic acids research · 2023Article
- Single-cell gene set scoring with nearest neighbor graph smoothed data (gssnng).Bioinformatics advances · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although an essential step, cell functional annotation often proves particularly challenging from single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most cases, these rely on techniques initially developed for bulk RNA sequencing or simply make use of marker genes identified from cell clustering followed by supervised annotation. To overcome these limitations and automatize the process, we have developed two novel methods, the single-cell gene set enrichment analysis (scGSEA) and the single-cell mapper (scMAP). scGSEA combines latent data representations and gene set enrichment scores to detect coordinated gene activity at single-cell resolution. scMAP uses transfer learning techniques to re-purpose and contextualize new cells into a reference cell atlas. Using both simulated and real datasets, we show that scGSEA effectively recapitulates recurrent patterns of pathways' activity shared by cells from different experimental conditions. At the same time, we show that scMAP can reliably map and contextualize new single-cell profiles on a breast cancer atlas we recently released. Both tools are provided in an effective and straightforward workflow providing a framework to determine cell function and significantly improve annotation and interpretation of scRNA-seq data.
Identifiers
What OpenQuestion holds
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.