21 papers · ranked by Valyu relevance
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…
Hettiarachchige Wijewardena, Saloni Bhatia, Namrata Bhattacharya, Debarka Sengupta + 2 more
Accurate cell type identification is critical for interpreting single-cell transcriptomic data and understanding complex biological systems. In this review, we discuss how natural language processing and large language models can enhance the accuracy and scalability of cell type annotation. We also highlight how…
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…
Sebastiano Cultrera di Montesano, Davide D’Ascenzo, Srivatsan Raghavan, Ava P. Amini + 2 more
Accurately annotating cell types is essential for extracting biological insight from single-cell RNA sequencing data. Although cell types are naturally organized into hierarchical ontologies, most computational models do not explicitly incorporate this structure into their training objectives. Here, we introduce a…
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…
Min Huang, Rishikesan Kamaleswaran
Accurate and scalable cell type annotation remains a challenge in single-cell transcriptomics, especially when datasets exhibit strong batch effects or contain previously unseen cell populations. Here we introduce SpikGPT, a hybrid deep learning framework that integrates scGPT-derived cell embeddings with a spiking…
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…
Rufus H Daw, Harry R Deijnen, Magnus Rattray, John R Grainger
Accurate cell type annotation is a critical step in gathering meaningful insights from single-cell RNA sequencing (scRNA-seq) datasets. Manual ‘expert’ annotation of clusters is considered the current gold standard (). This is often a time-consuming and laborious task, given the ever-increasing sample sizes of…
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…
Lin Yuan, Junjie Cao, Shengguo Sun, Siguo Wang + 3 more
A key challenge in single-cell RNA sequencing (scRNA-seq) data analysis is accurately and efficiently identifying the cell type of each cell. Cell type annotation for scRNA-seq data not only needs to overcome batch effects caused by various factors but also requires effective handling of large-scale scRNA-seq datasets.…
Yuling Zhu, Yunfei Hu, Manfei Bella Xie, Haoran Qin + 7 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, X. Zhang, Shuangsang Fang, Ran Li + 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 indepth…
Renchu Guan, Ji Qi, Xueting Wang, Chuyao Wang + 4 more
Single-cell resolution spatial transcriptomics (scST) simultaneously captures gene expression and spatial coordinates at an unprecedented scale, which provides powerful opportunities to dissect tissue architecture and cell-cell interactions. However, accurate cell type annotation remains challenging, particularly in…
Pengyu Xie, Rongjia Zhou, Zhilin Ou, Junyuan Zhang + 4 more
Cell type standardization plays a central role in integrating biological knowledge across single-cell studies. While standardized resources (e.g., Cell Ontology, Nomenclature Frameworks) provide unified vocabularies of cell populations, scientific publications and public datasets continue to use heterogeneous…
Sanghyun Jo, Seo Jin Lee, Seohyung Hong, Yoorim Gang + 3 more
Cell instance segmentation models trained on cell-specific datasets suffer severe performance drops on out-of-distribution cell types, while interactive foundation models overcome this through per-instance prompting at a cost that is prohibitively expensive for histopathology images containing hundreds to thousands of…
Arash Vashagh, Yasmin Vashagh
Many single-cell annotation tools refine an initial cell label using nearby cells or cluster-level voting. We study whether this refinement can be manipulated without changing the target cell. We introduce CohortHijack, a robustness audit that removes selected non-target cells from the query cohort while preserving the…
Satoshi Tsutsui, Winnie Pang, Shuting He, Bihan Wen
The microscopic examination of white blood cells (WBCs) plays a fundamental role in pathology and is essential for diagnosing blood disorders such as leukemia and anemia. To support further research on WBC images, multiple datasets have been proposed. However, they mainly annotate cell categories, and lack detailed…
Authors not listed
The Golgi apparatus (GA) orchestrates protein modification, trafficking, and secretion through highly dynamic remodeling, yet its proteomic complexity remains difficult to resolve in living systems. Here, we report CAT-Golgi, a genetically independent and light-controlled photocatalytic proximity labeling strategy for…
Authors not listed
Vertex models have been established as powerful tools for the cell-based simulation of epithelial tissues as they allow a detailed description of their mechanical development with respect to the properties of individual cells. Thus, they suit the challenges of simulating intestinal organoids which arise from the…
Francesco Benedetto, Roberto Basla, Luca Magri, Giacomo Boracchi
Training Deep Neural Networks for tracking individual cells in biomedical videos requires a large amount of annotated data. The annotation of videos for cell tracking is very time consuming and often requires domain expertise; this explains the limited availability of public annotated data to address important medical…
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The ability to track therapeutic cells is critical for advancing adoptive cell therapy (ACT). Positron emission tomography (PET) offers sensitive and quantitative imaging, yet improved cell radiolabeling strategies are sorely needed. We report a metabolic glycoengineering (MGE) approach that installs azide moieties on…