Single-cell RNA sequencing profiles cellular heterogeneity at atlas scale, making automated annotation essential. However, target datasets often contain novel cell types missing from incomplete references. We present scOLAR, an ontology-guided open-set framework that learns prototypes over the Cell Ontology and uses both reference and target expression to annotate known classes while detecting unfamiliar populations. Guided by ontology hierar-chies and decision-boundary regularization, scOLAR penalizes coarse-lineage misclassification and groups novel cells without requiring predefined cluster counts. Across benchmarks, scOLAR achieves a novelty-detection AUROC of 0.9726 and an average precision of 0.9871, enabling structured post-hoc lineage-level interpretation of populations absent from the reference.
This Review systematically integrates current conceptual frameworks, computational methodologies, and domain-specific best practices for cell-type annotation in cardiovascular single-cell research and offers practical recommendations and discuss future directions encompassing multimodal integration, spatial transcripto...
Lu Sun, Li Ma, Li-Zhi Chen et al.· International Journal of Bio...· 0 citations
GEOMeta provides a scalable resource and reproducible framework for metadata curation in the Gene Expression Omnibus, and benchmarked transcriptome representation models for predicting sex, age, tissue and disease from transcriptome embeddings.
Xiaodan Zhang, S. Paithankar, Jing Pu et al.· bioRxiv· 0 citations
Using standardized oracle inputs and Cohen’s κ, it is found that, within the ranges tested, the major paradigms achieved comparable accuracy, and the field’s near-term gains lie in strengthening infrastructure—prioritizing tool accessibility, standardized evaluation, and robustness to pipeline variation.
Oliver Wardhana, Zi-Yu Zeng, Xin Lu· bioRxiv· 0 citations
Unsupervised read-level differential analysis recovers established lncRNA biomarkers; uncovers new prognostic transposable-element reads in adrenocortical carcinoma and sarcomas; and extracts signals even from reads that fail to align.
Erkan Narmanli, Alexandre Lanau, Mara Neacsu et al.· bioRxiv· 0 citations
CARA is introduced, a cross-omics Bayesian framework that transfers cell type knowledge from scRNA-seq to scATAC-seq and detects novel cell types in single-cell DNA methylation data, demonstrating seamless extensibility to new modalities.
Lan Cao, Wenhao Zhang, Feng Zhou et al.· Genome Research· 0 citations
Single-cell RNA sequencing has transformed cell biology by enabling precise transcriptomic measurements of individual cells. The Sequence Read Archive (SRA) is the largest public repository of sequencing reads, yet much of it remains underutilized due to unstandardized metadata. Here, we introduce scBaseCount, a databa...
Nicholas D. Youngblut, Christopher Carpenter, Arshia Nayebnazar et al.· Cell· 7 citations
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