20 papers · ranked by Valyu relevance
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…
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…
Sheng Wang, Angela Oliveira Pisco, Jim Karkanias, Russ B. Altman
Single cell technologies have rapidly generated an unprecedented amount of data that enables us to understand biological systems at single-cell resolution. However, analyzing datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present…
Sheng Wang, Angela Oliveira Pisco, Aaron McGeever, Maria Brbic + 5 more
Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present…
Fang Yin, Qiao Jin, Guangzhi Xiong, Bowen Jin + 5 more
Cell type annotation is a key task in analyzing the heterogeneity of single-cell RNA sequencing data. Although recent foundation models automate this process, they typically annotate cells independently, without considering batch-level cellular context or providing explanatory reasoning. In contrast, human experts…
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…
Zehua Zeng, Hongwu Du
In recent years, single cell RNA sequencing has become a widely used technique to study cellular diversity and function. However, accurately annotating cell types from single cell data has been a challenging task, as it requires extensive knowledge of cell biology and gene function. The emergence of large language…
Tianhao Li, Zixuan Wang, Yuhang Liu, Sihan He + 2 more
'Yongqing Zhang'] Title: Abstract The rapid accumulation of single-cell RNA sequencing data has provided unprecedented computational resources for cell type annotation, significantly advancing our understanding of cellular heterogeneity. Leveraging gene expression profiles derived from transcriptomic data, researchers…
Aanchal Mongia, Diane C. Saunders, Yue J. Wang, Marcela Brissova + 6 more
Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput…
Yuren Mao, Yu Mi, Peigen Liu, Mengfei Zhang + 2 more
'Yunjun Gao'] Cell type annotation is critical for understanding cellular heterogeneity. Based on single-cell RNA-seq data and deep learning models, good progress has been made in annotating a fixed number of cell types within a specific tissue. However, universal cell annotation, which can generalize across tissues…
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…
Junhao Liu, Shengqian Xu, Lei Zhang, Jing Zhang
Cell Type Annotation Using Single-Cell Data Authors: ['Junhao Liu' 'Shengqian Xu' 'Lei Zhang' 'Jing Zhang'] Over the past decade, the revolution in singlecell sequencing has enabled the simultaneous molecular profiling of various modalities across thousands of individual cells, allowing scientists to investigate the…
Wenjin Ye, Yuanchen Ma, Jinjuan Xiang, Hongjie Liang + 6 more
LLM-Based Strategies Authors: ['Wenjin Ye' 'Yuanchen Ma' 'Jinjuan Xiang' 'Hongjie Liang' 'Tao Wang' 'Qiuling Xiang' 'Andy Peng Xiang' 'Song Wu' 'Weiqiang Li' 'Weijun Huang'] Reliability in cell type annotation is challenging in single-cell RNA-sequencing data analysis because both expert-driven and automated methods…
Dezheng Han, Yibin Jia, Ruxiao Chen, Wenjie Han + 2 more
'Jianbo Wang'] To enable precise and fully automated cell type annotation with large language models (LLMs), we developed a graph-structured feature–marker database to retrieve entities linked to differential genes for cell reconstruction. We further designed a multi-task workflow to optimize the annotation process.…
Euxhen Hasanaj, Jingtao Wang, Arjun Sarathi, Jun Ding + 1 more
'Ziv Bar-Joseph'] Cell type assignment is a major challenge for all types of high throughput single cell data. In many cases such assignment requires the repeated manual use of external and complementary data sources. To improve the ability to uniformly assign cell types across large consortia, platforms and…
Jonathan Karin, Reshef Mintz, Barak Raveh, Mor Nitzan
Single-cell and spatial genomics datasets can be organized and interpreted by annotating single cells to distinct types, states, locations, or phenotypes. However, cell annotations are inherently ambiguous, as discrete labels with subjective interpretations are assigned to heterogeneous cell populations based on noisy…
Tiago Lubiana, Paola Roncaglia, Chris Mungall, Ellen M. Quardokus + 3 more
'Joshua D. Fortriede' 'David Osumi-Sutherland' 'Alexander D. Diehl'] - 1 School of Pharmaceutical Sciences, University of São Paulo, São Paulo, SP, Brazil - 2 Ronin Institute for Independent Scholarship - 3 European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Wellcome Genome Campus…
Minxing Pang, Tarun Kanti Roy, Xiaodong Wu, Kai Tan
Cell segmentation and classification are critical tasks in spatial omics data analysis. Here we introduce CelloType, an end-to-end model designed for cell segmentation and classification for image-based spatial omics data. Unlike the traditional two-stage approach of segmentation followed by classification, CelloType…
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As the volume and diversity of bioactivity data in ChEMBL continues to grow, ensuring that assay metadata is standardized, interoperable, and machine-readable is critical for effective use in cheminformatics and ML applications. In this work, we present recent efforts to enhance the quality and granularity of bioassay…
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Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) is a powerful technique for molecular analysis of surfaces; however, its application of single cell studies has not been previously published. In the current work, a commercial DESI setup (DESI XS) coupled to a mass spectrometer was used to analyze…