13 papers · ranked by Valyu relevance
Gautam Ahuja, Alex Antill, Yi Su, Giovanni Marco Dall’Olio + 3 more
Cell type annotation remains a critical bottleneck, with current methods often inaccurate and requiring extensive manual validation, particularly in disease contexts. While large language models (LLMs) show promise, they can be unreliable due to hallucinations. We developed CyteType, a multi-agent framework that…
Yiran Song, Muyao Tang, Qi Liu, Haofei Wang + 3 more
Cell type annotation is critical for interpreting single-cell transcriptomic data but remains challenging due to uncertain cellular clustering granularity and inconsistent labeling across studies. Here we present GPTAnno, an automated, ontology-tree-guided, uncertainty-aware, hierarchical cell type annotation method…
Stephen R. Williams, Fedor Grab, Govinda M. Kamath, Yerdos Ordabayev + 13 more
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…
Mingyu Yang, Jing Qi, Minwen Lan, Jiacheng Huang + 1 more
Accurate cell-type annotation is a foundational task in single-cell RNA sequencing analysis, yet remains fundamentally challenged by cellular heterogeneity, gradual lineage transitions, and technical noise. As single-cell atlases expand in scale and resolution, most existing annotation approaches operate at a single…
Nimish Magre, Ebtisam Alshehri, Fedor Grab, Yerdos Ordabayev + 3 more
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…
Anthony Christidis, Andrew Ghazi, Smriti Chawla, Nitesh Turaga + 2 more
Although cell type annotation has become an integral part of single-cell analysis workflows, the assessment of computational annotations remains challenging. Many annotation tools transfer labels from an annotated reference dataset to a new query dataset of interest, but blindly transferring labels from one dataset to…
Rufus H. Daw, Harry R. Deijnen, Magnus Rattray, John R. Grainger
Single-cell RNA sequencing (scRNA-seq) cell annotation techniques rely on the matching of known defining marker genes to a given cell population. However, these methods may lack robustness to dynamic fluctuations in cell marker expression between patients, samples and pathologies. The advent of easy-to-implement…
Wade Boohar, Bowen Wang, Zachary Thomas, Anna Nogalska + 1 more
Recent advances in spatial omics enable high-resolution, multiplexed in situ imaging of gene and protein expression. A major challenge in analyzing these data is cell annotation, especially in complex tissues with limited molecular markers, overlapping marker expression, or rare cell types such as stem cells. Here, we…
Noam H. Rotenberg, Robert Leaman, Rezarta Islamaj, Helena Kuivaniemi + 10 more
The variety of cell phenotypes identified by single-cell technologies is rapidly expanding, yet this knowledge is dispersed across the scientific literature and incompletely represented in structured resources. We present the CellLink corpus, a manually annotated collection of over 22,000 mentions of human and mouse…
Lei Shi, Min Dai, Yongbo Zhang, Song Wu + 2 more
Single-cell omics and spatial omics technologies are nowadays widely used in biological and medical research. In both single-cell and spatial omics data analysis, accurate cell type annotation is a key step for downstream analysis and scientific discoveries. However, high-quality cell annotation usually requires…
Yuling Zhu, Yunfei Hu, Manfei Bella Xie, Haoran Qin + 8 more
Spatial transcriptomics enables the quantification of gene expression within its native tissue context, providing unprecedented insight into tissue architecture, cellular ecosystems, and local cell–cell interactions at regional and single-cell resolution. Accurate cell type annotation is a critical prerequisite for…
Haoran Wang, Xuanyi Zhang, Shuangsang Fang, Longke Ran + 4 more
Recent advancements in single-cell multi-omics, particularly RNA-seq, have provided profound insights into cellular heterogeneity and gene regulation. While pre-trained language model (PLM) paradigm based single-cell foundation models have shown promise, they remain constrained by insufficient integration of in-depth…
Christopher Beltz, Zekai Qiu, Leon Sadowski, Joscha A. Kraske + 38 more
Accurate immune cell classification is essential for interpreting single-cell RNA sequencing (scRNA-seq) data. However, progress is constrained by the lack of independent, high-resolution benchmarks, as the routine integration of datasets introduces statistical dependencies that artificially inflate model…