ArticleGenes2019
SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 174 papers, 1 of them a synthesis that pooled it.
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
174 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Single-cell RNA sequencing offers novel perspectives in viral infection research.Frontiers in cellular and infection microbiology · 2026Pooled it
- Molecular signals associated with intraperitoneal chemotherapy resistance in gastric cancer with peritoneal metastasis through PIPS GC trial integrated translational research.Scientific reports · 2025Trial
- celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool.bioRxiv : the preprint server for biology · 2026Article
- Single-cell and spatial transcriptomics reveals the spatiotemporal trajectory of the small peptide TAP4 in delaying postharvest fruit senescence.Plant communications · 2026Article
- Cell-Specific Expression and Cellular Compartmental Regulation in Camptothecin Biosynthesis.Plant biotechnology journal · 2026Article
- Article
- scLGGCL: Label-Guided Graph Contrastive Learning for Single-Cell Fusion Clustering.Interdisciplinary sciences, computational life sciences · 2026Article
- Single-Cell Transcriptomics Reveals the FA9-VAP Module Regulating Fatty Acid Accumulation in Soybean Seeds.Plant biotechnology journal · 2026Article
- Immune-Responsive Bioinks Enable Vision-GuidedAdvanced functional materials · 2026Article
- Integrative evidence-knowledge marker selection enhances LLM-based cell type annotation in single-cell RNA-seq analysis.BioData mining · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- scMapNet: Marker-based cell type annotation of scRNA-seq data via vision transfer learning with tabular-to-image transformations.Journal of advanced research · 2026Article
- Classification of indeterminate and off-target cell types within human kidney organoid differentiation.iScience · 2026Article
- scUmaper: An automated framework for doublet removal and cell-type annotation in single-cell transcriptomics.iScience · 2026Article
- Robust semi-supervised scRNA-seq integration from virtual adversarial learning.bioRxiv : the preprint server for biology · 2026Article
- ProtoCloud: A prototypical self-explaining model for single-cell analysis.Cell genomics · 2026Article
- Computational modelling of cell identity.The Biochemical journal · 2026Review
- Transformers for single-cell RNA sequencing: a survey.Briefings in bioinformatics · 2026Review
- Article
- A survey on large language models in biology and chemistry.Experimental & molecular medicine · 2026Review
114 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore prior knowledge of transcriptomes and the probable structures of the data. Moreover, cell identification heavily relies on subjective and possibly inaccurate human inspection afterwards. To address these analytical challenges, we developed SCINA (Semi-supervised Category Identification and Assignment), a semi-supervised model that exploits previously established gene signatures using an expectation-maximization (EM) algorithm. SCINA is applicable to scRNA-Seq and flow cytometry/CyTOF data, as well as other data of similar format. We applied SCINA to a wide range of datasets, and showed its accuracy, stability and efficiency, which exceeded most popular unsupervised approaches. SCINA discovered an intermediate stage of oligodendrocytes from mouse brain scRNA-Seq data. SCINA also detected immune cell population changes in cytometry data in a genetically-engineered mouse model. Furthermore, SCINA performed well with bulk gene expression data. Specifically, we identified a new kidney tumor clade with similarity to FH-deficient tumors (FHD), which we refer to as FHD-like tumors (FHDL). Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.
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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.