Ordabayev, Y. | bioRxiv Science

bioRxiv2026

A supervised ontology-aware cell annotation method forsingle-cell transcriptomic data

Many single-cell RNA-seq annotation methods ignore the hierarchical nature of cell type classification. We present a probability propagation strategy that enforces ontological consistency and improves performance when applied to existing models without retraining. Combined with a lightweight logistic regression model trained on 42 million human cells, this yields SOCAM, a fast and interpretable classifier. We also introduce a hop-based F1 score for ontology-aware evaluation. Code and models are available open source.

bioinformatics↗

bioRxiv2025

Accelerating scRNA-seq analysis: automated cell type annotation using representation learning and vector search

Cell type annotation in single-cell RNA sequencing (scRNA-seq) experiments is the fundamental step of assigning cell types to individual cells or clusters of cells based on their gene expression profiles. This process is crucial for developing biological insights from scRNA-seq experiments. We present a service that automates cell type annotation for 10x Genomics single-cell gene expression samples, enabling researchers to rapidly and accurately categorize cells within a sample. This service operates on the basis of reverse search: it compares each cells gene expression profile against the Chan Zuckerberg CELL by GENE (CZ CELLxGENE) Census, a comprehensive repository of published scRNA-seq datasets enriched with community-annotated cell types, and yields cell type annotations through summarizing the labels associated with similar cells. The annotation algorithm employed in this service avoids reliance on predefined marker genes or tissue-specific references, providing both fine-grained and coarse annotations. These initial annotations can be further refined by investigators to suit their specific research needs.

bioinformatics↗