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Journal articles on the topic 'Automatic cell types annotation'

Author: Grafiati

Published: 25 June 2021

Last updated: 29 July 2025

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1

Hia, Nazifa Tasnim, and Sumon Ahmed. "Automatic cell type annotation using supervised classification: A systematic literature review." Systematic Literature Review and Meta-Analysis Journal 3, no. 3 (2022): 99–108. http://dx.doi.org/10.54480/slrm.v3i3.45.

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Single-cell sequencing gives us the opportunity to analyze cells on an individual level rather than at a population level. There are different types of sequencing based on the stage and portion of the cell from where the data are collected. Among those Single Cell RNA seq is most widely used and most application of cell type annotation has been on Single-cell RNA seq data. Tools have been developed for automatic cell type annotation as manual annotation of cell type is time-consuming and partially subjective. There are mainly three strategies to associate cell type with gene expression profile

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2

Xu, Yang, Simon J. Baumgart, Christian M. Stegmann, and Sikander Hayat. "MACA: marker-based automatic cell-type annotation for single-cell expression data." Bioinformatics 38, no. 6 (2021): 1756–60. http://dx.doi.org/10.1093/bioinformatics/btab840.

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Abstract Summary Accurately identifying cell types is a critical step in single-cell sequencing analyses. Here, we present marker-based automatic cell-type annotation (MACA), a new tool for annotating single-cell transcriptomics datasets. We developed MACA by testing four cell-type scoring methods with two public cell-marker databases as reference in six single-cell studies. MACA compares favorably to four existing marker-based cell-type annotation methods in terms of accuracy and speed. We show that MACA can annotate a large single-nuclei RNA-seq study in minutes on human hearts with ∼290K ce

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3

Shao, Xin, Jie Liao, Xiaoyan Lu, Rui Xue, Ni Ai, and Xiaohui Fan. "scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data." iScience 23, no. 3 (2020): 100882. http://dx.doi.org/10.1016/j.isci.2020.100882.

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Tang, Yachen, Xuefeng Li, and Mingguang Shi. "LIDER: cell embedding based deep neural network classifier for supervised cell type identification." PeerJ 11 (August 16, 2023): e15862. http://dx.doi.org/10.7717/peerj.15862.

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Background Automatic cell type identification has been an urgent task for the rapid development of single-cell RNA-seq techniques. Generally, the current approach for cell type identification is to generate cell clusters by unsupervised clustering and later assign labels to each cell cluster with manual annotation. Methods Here, we introduce LIDER (celL embeddIng based Deep nEural netwoRk classifier), a deep supervised learning method that combines cell embedding and deep neural network classifier for automatic cell type identification. Based on a stacked denoising autoencoder with a tailored

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Xiong, Yi-Xuan, Meng-Guo Wang, Luonan Chen, and Xiao-Fei Zhang. "Cell-type annotation with accurate unseen cell-type identification using multiple references." PLOS Computational Biology 19, no. 6 (2023): e1011261. http://dx.doi.org/10.1371/journal.pcbi.1011261.

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The recent advances in single-cell RNA sequencing (scRNA-seq) techniques have stimulated efforts to identify and characterize the cellular composition of complex tissues. With the advent of various sequencing techniques, automated cell-type annotation using a well-annotated scRNA-seq reference becomes popular. But it relies on the diversity of cell types in the reference, which may not capture all the cell types present in the query data of interest. There are generally unseen cell types in the query data of interest because most data atlases are obtained for different purposes and techniques.

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Liu, Huaitian, Alexandra Harris, Brittany Jenkins-Lord, et al. "Abstract LB240: Cell type annotation using singleR with custom reference for single-nucleus multiome data derived from frozen human breast tumors." Cancer Research 84, no. 7_Supplement (2024): LB240. http://dx.doi.org/10.1158/1538-7445.am2024-lb240.

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Abstract Single-nucleus joint ATAC- and RNA-sequencing (snMultiome) can be used to identify functionally divergent cell subpopulations based on their transcriptomic and epigenetic profiles within complex samples. Accurate cell type annotation is critical to successful snMultiome data analysis. Several computational methods have been developed for automatic annotation. Traditional cell type annotation methods initially cluster cells using unsupervised learning methods based on the gene expression profiles, then label the clusters using aggregated cluster-level expression profiles and marker gen

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Zhang, Yuping, Gabriel Cruz, Hanbyul Cho, et al. "Abstract 1063: CellMap: a comprehensive human single cell gene expression reference for automated cell annotation and cancer cell-of-origin analysis." Cancer Research 85, no. 8_Supplement_1 (2025): 1063. https://doi.org/10.1158/1538-7445.am2025-1063.

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Abstract In single-cell transcriptomic analysis, accurate cell type annotation forms the essential basis for all downstream analysis and data interpretation. While time consuming manual annotation requires extensive prior knowledge, available automated cell annotation tools lack a unified, curated human cell reference to ensure successful identification of all cell types present in any given dataset. To fill in the gap we create “CellMap” a comprehensive single cell gene expression reference database of known cell types across most human tissues by compiling, curating and integrating single ce

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Doddahonnaiah, Deeksha, Patrick J. Lenehan, Travis K. Hughes, et al. "A Literature-Derived Knowledge Graph Augments the Interpretation of Single Cell RNA-seq Datasets." Genes 12, no. 6 (2021): 898. http://dx.doi.org/10.3390/genes12060898.

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Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have advanced rapidly in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedic

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Pham, Son, Tri Le, Tan Phan, et al. "484 Bioturing browser: interactively explore public single cell sequencing data." Journal for ImmunoTherapy of Cancer 8, Suppl 3 (2020): A520. http://dx.doi.org/10.1136/jitc-2020-sitc2020.0484.

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BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual dat

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Mao, Shunfu, Yue Zhang, Georg Seelig, and Sreeram Kannan. "CellMeSH: probabilistic cell-type identification using indexed literature." Bioinformatics 38, no. 5 (2021): 1393–402. http://dx.doi.org/10.1093/bioinformatics/btab834.

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Abstract Motivation Single-cell RNA sequencing (scRNA-seq) is widely used for analyzing gene expression in multi-cellular systems and provides unprecedented access to cellular heterogeneity. scRNA-seq experiments aim to identify and quantify all cell types present in a sample. Measured single-cell transcriptomes are grouped by similarity and the resulting clusters are mapped to cell types based on cluster-specific gene expression patterns. While the process of generating clusters has become largely automated, annotation remains a laborious ad hoc effort that requires expert biological knowledg

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Chuang, Tsung Hsien, Liang-Chuan Lai, Tzu-Pin Lu, Mong-Hsun Tsai, Hsiang-Han Chen, and Eric Y. Chuang. "Abstract 878: Enhancing single-cell RNA sequencing analysis in cancer research: A machine learning framework based on LightGBM for automated cell type annotation." Cancer Research 84, no. 6_Supplement (2024): 878. http://dx.doi.org/10.1158/1538-7445.am2024-878.

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Abstract Single-cell RNA sequencing (scRNA-seq) has been widely used in cancer research to understand the complex gene expression diversity and cancer heterogeneity. However, manual annotation of cell types in the scRNA-seq pipeline is time-consuming and depends on the expertise of analyzers, which can significantly influence the results of downstream analyses. To address this problem, we proposed a novel machine learning framework utilizing the LightGBM model for automated and efficient cell-type annotation of scRNA-seq. Two independent scRNA-seq datasets of non-small cell lung cancer (NSCLC)

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Kim, Seongryong, Sungmin Cheong, Suho Lee, Yeju Kim, and Jong-Eun Park. "Abstract 6265: Cross-tissue atlas of human disease identifies tumor-specific components in tumor microenvironment." Cancer Research 85, no. 8_Supplement_1 (2025): 6265. https://doi.org/10.1158/1538-7445.am2025-6265.

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Abstract Human diseases affect various cell types within the local niche, but the multicellular responses of human diseases have not been collectively assessed across organs. Here, we categorize multicellular responses of human diseases based on a remapped human cell atlas, incorporating 12 million single-cell transcriptome profiles acquired from an automated data collection pipeline. Additional curation expands the collection up to 46 million cells, covering 160 disease conditions across 49 distinct organ identities. From the re-aligned atlas of single-cell transcriptome data, we constructed

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Wong, Siao-Han, Benedikt Brors, and Sonja Loges. "Abstract B053: Empowering AI-driven prediction of the tumor microenvironment from histopathology images via molecular annotation." Clinical Cancer Research 31, no. 13_Supplement (2025): B053. https://doi.org/10.1158/1557-3265.aimachine-b053.

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Abstract The tumor microenvironment (TME) actively contribute to tumor development and treatment response. The interplay between tumor cells, immune cells, fibroblasts and blood vessels contribute to immune escape and drug resistance. Prior to treatment, higher tumor infiltrating lymphocytes correlate with better survival, while greater stromal content is linked to poor survival. Studying the composition and dynamics of the TME is essential for improving patient stratification, however a scalable tool for addressing this question is still lacking. Spatially resolved omics technologies allow fo

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Patino, Cesar A., Prithvijit Mukherjee, Vincent Lemaitre, Nibir Pathak, and Horacio D. Espinosa. "Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform." SLAS TECHNOLOGY: Translating Life Sciences Innovation 26, no. 1 (2021): 26–36. http://dx.doi.org/10.1177/2472630320982320.

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Single-cell delivery platforms like microinjection and nanoprobe electroporation enable unparalleled control over cell manipulation tasks but are generally limited in throughput. Here, we present an automated single-cell electroporation system capable of automatically detecting cells with artificial intelligence (AI) software and delivering exogenous cargoes of different sizes with uniform dosage. We implemented a fully convolutional network (FCN) architecture to precisely locate the nuclei and cytosol of six cell types with various shapes and sizes, using phase contrast microscopy. Nuclear st

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Shapiro, Joshua A., Stephanie J. Spielman, Allegra G. Hawkins, et al. "Abstract 2615: The Open Single-cell Pediatric Cancer Atlas project: Collaborative analysis of pediatric tumor data." Cancer Research 85, no. 8_Supplement_1 (2025): 2615. https://doi.org/10.1158/1538-7445.am2025-2615.

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Abstract The Open Single-cell Pediatric Cancer Atlas (OpenScPCA) project is an open, collaborative project created to analyze publicly available data from the Single-cell Pediatric Cancer Atlas (ScPCA) Portal, with the goal of improving the quality and usability of single-cell pediatric cancer data and driving insights into pediatric cancer biology through deeper analysis of available data sets. The ScPCA Portal (https://scpca.alexslemonade.org/), developed and maintained by Alex’s Lemonade Stand Foundation (ALSF), is an open-source data resource for single-cell and single-nuclei RNA sequencin

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Ma, Ji, Qiang He, Qian Wu, Lili Feng, Y. Lynn Wang, and Linna Xie. "Uncovering Therapeutic Resistance in BPDCN through Single-Cell Transcriptome Profiling." Blood 144, Supplement 1 (2024): 1557. https://doi.org/10.1182/blood-2024-204227.

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Background: Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare, aggressive hematologic malignancy arising from plasmacytoid dendritic cells. The most commonly affected sites include the skin (65%), bone marrow (51%), and blood (45%). Tumor cells typically exhibit immunophenotypes of CD4+, CD45+, CD56+, CD123+, HLA-DR+, and TCL-1+. Despite initial treatment responsiveness, BPDCN frequently relapses, posing significant challenges in patient management. Although Next-Generation Sequencing (NGS) and genome-wide DNA methylation analysis have elucidated the molecular characteristics of B

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Bell, Alexander T., Kohei Fujikura, Jacob Stern, et al. "Abstract 637: Spatial transcriptomics for FFPE characterizes the molecular and cellular architecture of malignant changes in pancreatic pre-malignant lesions." Cancer Research 82, no. 12_Supplement (2022): 637. http://dx.doi.org/10.1158/1538-7445.am2022-637.

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Abstract We have optimized an experimental and computational pipeline to adapt spatial transcriptomics (ST) approaches based upon the Visium (10x Genomics) technology to infer cellular composition and intercellular interactions of FFPE clinical specimens. We apply this technology to deliver an approach to examine pancreatic intraepithelial neoplasia (PanIN) to identify intrinsic and extrinsic mechanisms that are associated with the progression of these pre-malignant lesions to invasive carcinoma. Currently, most pancreatic cancers are diagnosed at an advanced stage that reflects in dismal surv

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Rakesh and Puru Naik Dr. "A study on automated unsupervised identification of cone photoreceptor cells in adaptive optics scanning laser ophthalmoscope images." International Journal of Advance Research in Multidisciplinary 1, no. 1 (2023): 729–37. https://doi.org/10.5281/zenodo.13897058.

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Precise and effective identification of cone photoreceptor cells is essential for comprehending retinal structure and identifying vision-related disorders. Adaptive optics scanning laser ophthalmoscopy (AOSLO) enables the capture of detailed images of the retina, allowing for the visualization of individual photoreceptor cells. Nevertheless, the process of manually identifying these cells is laborious and susceptible to mistakes. This paper presents a novel approach for automatically and without human supervision identifying cone photoreceptor cells in AOSLO pictures. The system utilizes unsup

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Chang, Cadence, Egmidio Medina, Sarah Samordnitsky, et al. "Abstract 2505: Semi-automated image registration and cell typing integrates multiplexed imaging data to investigate the tumor microenvironment in clinical biopsies." Cancer Research 85, no. 8_Supplement_1 (2025): 2505. https://doi.org/10.1158/1538-7445.am2025-2505.

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Abstract Introduction: Spatial profiling technologies are individually limited by the number of proteins evaluated, making it beneficial to integrate imaging analysis across multiple slides from a single biopsy. Here, we describe a semi-automated workflow for the scaled image registration and downstream analysis of clinical melanoma biopsies treated with immunotherapy and evaluated across multiplexed protein platforms. Methods: Clinical biopsies (n=119) from patients with melanoma (N=39) were assayed on the Vectra Polaris platform. Two sequential slides from each biopsy were individually proce

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Wasser, Martin, Joo Guan Yeo, Pavanish Kumar, et al. "The EPIC data analytics platform for clinical mass cytometry." Journal of Immunology 204, no. 1_Supplement (2020): 159.7. http://dx.doi.org/10.4049/jimmunol.204.supp.159.7.

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Abstract Single cell technologies, such as high-dimensional cytometry, promise to enable the discovery of immune cell populations that will serve as clinical biomarkers. Recently, we reported the creation of a reference database of the healthy immune system from birth to old age and the development of the EPIC (Extended Poly-dimensional Immunome characterisation) data mining platform (Nature Biotech, accepted). Here we extend the analytics pipeline to facilitate detection of clinically stratifying cell populations in mass cytometry (CyTOF or cytometry by time-of-flight) data. Data structures c

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Balzategui, Julen, Luka Eciolaza, and Daniel Maestro-Watson. "Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network." Sensors 21, no. 13 (2021): 4361. http://dx.doi.org/10.3390/s21134361.

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Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, with non-destructive inspection and traceability of 100% of produced parts. Developing robust fault detection and classification models from the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patterns and the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting these peculiarities, in the context of solar cell manufacturing. The methodology is divided into two p

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Amiji, Hatim, Todd Brinsley Sheridan, Jeffrey Chuang, and Jill Carol Rubinstein. "Deep learning tumor heterogeneity metric from histopathology images vs next generation sequencing-derived scores for colon cancer prognostication." Journal of Clinical Oncology 41, no. 16_suppl (2023): 3537. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.3537.

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3537 Background: Tumor heterogeneity is as an important determinant of clinical behavior in many cancer types, with increased heterogeneity thought to confer inferior clinical outcome. Sequencing based assessment of colon cancer has been used to quantify tumor heterogeneity and correlate it with survival, but is sensitive to the mutation calling algorithm utilized. Digitization of histopathology slides allows application of deep learning methods for image analysis. Automated slide annotation and feature extraction provides numeric representations of underlying phenotype from which tumor hetero

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Lecat, Catherine SY, Yeman Brhane Hagos, Dominic Patel, et al. "Spatial Mapping of Myeloma Bone Marrow Microenvironment Using a Deep Learning Approach." Blood 142, Supplement 1 (2023): 903. http://dx.doi.org/10.1182/blood-2023-179810.

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Background: Interactions between multiple myeloma (MM) cells and the bone marrow (BM) microenvironment influence disease evolution and drug resistance. Information is largely based on BM aspirates, which lack topological information and can be affected by haemodilution. To examine the spatial relationships between tumour and immune cells in the MM immune tumour microenvironment (iTME), we used multiplex immunohistochemistry (MIHC) coupled with deep learning (DL) image analysis for detailed visualization and unbiased automated analysis of BM components. Methods: BM trephines from patients (at a

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Friedmann, Drew, Albert Pun, Eliza L. Adams, et al. "Mapping mesoscale axonal projections in the mouse brain using a 3D convolutional network." Proceedings of the National Academy of Sciences 117, no. 20 (2020): 11068–75. http://dx.doi.org/10.1073/pnas.1918465117.

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The projection targets of a neuronal population are a key feature of its anatomical characteristics. Historically, tissue sectioning, confocal microscopy, and manual scoring of specific regions of interest have been used to generate coarse summaries of mesoscale projectomes. We present here TrailMap, a three-dimensional (3D) convolutional network for extracting axonal projections from intact cleared mouse brains imaged by light-sheet microscopy. TrailMap allows region-based quantification of total axon content in large and complex 3D structures after registration to a standard reference atlas.

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Kapoor, Muskan, Christopher K. Tuggle, Tony Burdett, et al. "PSII-6 Computational Tools and Resources for Analysis and Exploration of Single-Cell Rnaseq Data in Agriculture." Journal of Animal Science 101, Supplement_2 (2023): 267–68. http://dx.doi.org/10.1093/jas/skad341.303.

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Abstract The agriculture genomics community has numerous data submission standards available but little experience describing and storing single-cell (e.g., scRNAseq) data. Other single-cell genomics infrastructure efforts, such as the Human Cell Atlas Data Coordination Platform (HCA DCP), have resources that could benefit our community. For example, the HCA DCP is integrated with Terra, a cloud-native workbench for computational biology developed by Broad, Verily, and Microsoft that houses tools for scGenomics analysis. We will describe a pilot-scale project to determine if our current metada

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Shapiro, Joshua A., Stephanie J. Spielman, Deepashree V. Prasad, et al. "Abstract B075: The Open Single-cell Pediatric Cancer Atlas project: Collaborative analysis of pediatric tumor data." Cancer Research 84, no. 17_Supplement (2024): B075. http://dx.doi.org/10.1158/1538-7445.pediatric24-b075.

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Abstract The Open Single-cell Pediatric Cancer Atlas (OpenScPCA) project is an open, collaborative project created to analyze publicly available data from the Single-cell Pediatric Cancer Atlas (ScPCA) Portal, with the goal of improving the quality and usability of single-cell pediatric cancer data and driving insights into pediatric cancer biology through deeper analysis of available data sets. The ScPCA Portal (https://scpca.alexslemonade.org/), developed and maintained by Alex’s Lemonade Stand Foundation (ALSF), is an open-source data resource for single-cell and single-nuclei RNA sequencin

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Bozorgui, Behnaz, Zeynep Dereli, Guillaume Thibault, John N. Weinstein, and Anil Korkut. "Abstract 3765: Single cell spatial proteomics analysis and computational evaluation pipeline." Cancer Research 84, no. 6_Supplement (2024): 3765. http://dx.doi.org/10.1158/1538-7445.am2024-3765.

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Abstract Resolving tissue and proteomic heterogeneity is critical to decoding the structure and function of tumor-immune microenvironment (TIME). Such understanding requires profiling of tumor and immune cell proteomic features with spatial resolution at the single-cell level. Although such spatially resolved methods and data sets are becoming increasingly available, analytical and computational methods that can extract the highly complex features and interactions within TIME are lacking. To address that problem, we have developed a computational pipeline we call the Spatial Proteomics Analysi

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Xu, Chuanyun, Mengwei Li, Gang Li, Yang Zhang, Chengjie Sun, and Nanlan Bai. "Cervical Cell/Clumps Detection in Cytology Images Using Transfer Learning." Diagnostics 12, no. 10 (2022): 2477. http://dx.doi.org/10.3390/diagnostics12102477.

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Cervical cancer is one of the most common and deadliest cancers among women and poses a serious health risk. Automated screening and diagnosis of cervical cancer will help improve the accuracy of cervical cell screening. In recent years, there have been many studies conducted using deep learning methods for automatic cervical cancer screening and diagnosis. Deep-learning-based Convolutional Neural Network (CNN) models require large amounts of data for training, but large cervical cell datasets with annotations are difficult to obtain. Some studies have used transfer learning approaches to hand

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Kang Lai, Colwyn Jia, Wei Kit Tan, Marcia Zhang, et al. "Abstract 6316: Predictive performance comparison of foundational and CNN models for single-cell immune profiling." Cancer Research 85, no. 8_Supplement_1 (2025): 6316. https://doi.org/10.1158/1538-7445.am2025-6316.

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Abstract Introduction: Characterizing the immune contexture is key to understanding the tumor microenvironment, developing biomarkers, and guiding therapeutic strategies. In our previous work on colorectal cancer (CRC), we used a pathologist-guided approach to annotate/count eosinophils and lymphocytes. Their abundance and spatial relationships with tumor cells revealed prognostic significance. While clinically valuable, identifying this demands significant pathologist effort. Advancements in AI, particularly foundational models trained on millions of histology images, have driven significant

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Tan, Benedict, Yi Yang, Chun Chau Lawrence Cheung, et al. "626 Dissecting the spatial heterogeneity of SARS-CoV-2-infected tumour microenvironment reveals a lymphocyte-dominant immune response in a HBV-associated HCC patient with COVID-19 history." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (2021): A656. http://dx.doi.org/10.1136/jitc-2021-sitc2021.626.

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BackgroundWe previously reported the presence of SARS-CoV-2 RNA in the hepatic tissues of recovered patients1 but the spatial immune profile of SARS-CoV-2 infection remains poorly understood. To address this, here we performed deep spatial profiling in tumour-adjacent normal hepatic tissue from a HBV-associated hepatocellular carcinoma (HCC) patient with history of COVID-19.MethodsWe obtained tissue from curative resection of a HCC patient 85 days post-recovery from COVID-19. Spatial immune profiling was performed by multiplex immunohistochemistry (mIHC)2 and more deeply using the Visium spati

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Hryhorenko, N., N. Larionov, and V. Bredikhin. "RESEARCH OF THE PROCESS OF VISUAL ART TRANSMISSION IN MUSIC AND THE CREATION OF COLLECTIONS FOR PEOPLE WITH VISUAL IMPAIRMENTS." Municipal economy of cities 6, no. 180 (2023): 2–6. http://dx.doi.org/10.33042/2522-1809-2023-6-180-2-6.

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This article explores the creation of music through the automated generation of sounds from images. The developed automatic image sound generation method is based on the joint use of neural networks and light-music theory. Translating visual art into music using machine learning models can be used to make extensive museum collections accessible to the visually impaired by translating artworks from an inaccessible sensory modality (sight) to an accessible one (hearing). Studies of other audio-visual models have shown that previous research has focused on improving model performance with multimo

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Eweje, Feyisope, Zhe Li, Matthew Gopaulchan, et al. "Use of artificial intelligence–based digital pathology to predict outcomes for immune checkpoint inhibitor therapy in advanced gastro-esophageal cancer." Journal of Clinical Oncology 42, no. 16_suppl (2024): 4013. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.4013.

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4013 Background: Accurate prediction of response and survival outcomes with anti-PD-1/PD-L1 immune checkpoint inhibition (ICI) remains a significant challenge in gastro-esophageal cancers. In this study, we use an artificial intelligence (AI)-based single-cell analysis of digitized whole-slide H&E images (WSIs) to predict objective response and survival benefit of ICI in two independent cohorts of gastro-esophageal cancer patients. Methods: WSIs were obtained from 82 ICI-treated advanced gastroesophageal cancer patients (gastric, esophageal, and gastro-esophageal junction adenocarcinoma an

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Mai, Yun, Kyeryoung Lee, Zongzhi Liu, et al. "Phenotyping of clinical trial eligibility text from cancer studies into computable criteria in electronic health records." Journal of Clinical Oncology 39, no. 15_suppl (2021): 6592. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.6592.

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6592 Background: Clinical trial phenotyping is the process of extracting clinical features and patient characteristics from eligibility criteria. Phenotyping is a crucial step that precedes automated cohort identification from patient electronic health records (EHRs) against trial criteria. We establish a clinical trial phenotyping pipeline to transform clinical trial eligibility criteria into computable criteria and enable high throughput cohort selection in EHRs. Methods: Formalized clinical trial criteria attributes were acquired from a natural-language processing (NLP)-assisted approach. W

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Song, Hanbing, Junxiang Xu, Paul Allegakoen, et al. "Abstract 7506: A gene program association study (GPAS) in prostate cancer reveals novel gene modules associated with plasticity and metastasis." Cancer Research 85, no. 8_Supplement_1 (2025): 7506. https://doi.org/10.1158/1538-7445.am2025-7506.

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Abstract Objective: Prostate cancer (PCa) is a heterogeneous disease both within and across individuals. Genomic alterations, epigenetic changes, and interactions with cells in the tumor microenvironment (TME) contribute to different states in tumor cells. Single-cell RNAseq (scRNAseq) can provide insights into the transcriptional heterogeneity of tumor cells and cell types in the TME. However, most scRNAseq studies are limited by small sample sizes. Here, we performed the first gene program association study in PCa to identify gene modules and immune cell types associated with intra-tumoral h

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Eweje, Feyisope, Zhe Li, Yuchen Li, et al. "Digital pathology–based AI spatial biomarker to predict outcomes for immune checkpoint inhibitors in advanced non-small cell lung cancer." Journal of Clinical Oncology 43, no. 16_suppl (2025): 8569. https://doi.org/10.1200/jco.2025.43.16_suppl.8569.

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8569 Background: Accurate prediction of outcomes with anti-PD-1/PD-L1 immune checkpoint inhibition (ICI) remains a significant challenge in non-small cell lung cancer (NSCLC). In this study, we develop an artificial intelligence (AI) approach for single-cell analysis of H&E-stained whole-slide images (WSIs) to predict objective response and clinical benefit of ICI in two independent cohorts of NSCLC patients. Methods: For biomarker discovery, we analyzed WSIs and clinical data from 118 advanced lung cancer patients at Stanford University. Of these, 46 patients (39%) were treated with ICI m

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Magidey, Ksenia, Ksenya Kveler, Rachelly Normand, et al. "A Unique Crosstalk between Tumor Cells and Hematopoietic Stem Cells Reveals a Myeloid Differentiation Pattern Signature Contributing to Metastasis." Blood 134, Supplement_1 (2019): 2465. http://dx.doi.org/10.1182/blood-2019-128126.

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Metastasis is the major cause of death in cancer patients. Recent studies have demonstrated that the crosstalk between different host and tumor cells in the tumor microenvironment regulates tumor progression and metastasis. Specifically, immune cell myeloid skewing is a prominent promoter of metastasis. While previous studies have demonstrated that the recruitment of myeloid cells to tumors is a critical step in dictating tumor fate, the reservoir of these cells in the bone marrow (BM) compartment and their differentiation pattern has not been explored. Here we utilized a unique model system c

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Wang, Panwen, Haidong Dong, Yue Yu, et al. "Abstract 4959: Immunopipe: A comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline." Cancer Research 84, no. 6_Supplement (2024): 4959. http://dx.doi.org/10.1158/1538-7445.am2024-4959.

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Abstract Introduction: Single-cell RNA-sequencing (scRNA-seq) pairing with single-cell T-Cell-Receptor-sequencing (scTCR-seq) enables profiling of gene expression and TCR repertoires in individual T cells, elevating our understanding of T-cell-mediated immunity. Here, we present immunopipe, a comprehensive and flexible pipeline (https://github.com/pwwang/immunopipe) for analyzing the paired data. Besides the command-line tool, it offers a user-friendly web interface that allows users with varying programming expertise to configure, initiate, and monitor the pipeline. By combining extensive fun

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Englbrecht, Fabian, Iris E. Ruider, and Andreas R. Bausch. "Automatic image annotation for fluorescent cell nuclei segmentation." PLOS ONE 16, no. 4 (2021): e0250093. http://dx.doi.org/10.1371/journal.pone.0250093.

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Dataset annotation is a time and labor-intensive task and an integral requirement for training and testing deep learning models. The segmentation of images in life science microscopy requires annotated image datasets for object detection tasks such as instance segmentation. Although the amount of annotated image data has been steadily reduced due to methods such as data augmentation, the process of manual or semi-automated data annotation is the most labor and cost intensive task in the process of cell nuclei segmentation with deep neural networks. In this work we propose a system to fully aut

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da Costa, André Luiz N. Targino, Jingxian Liu, Chia-Kuei Mo, et al. "Abstract 2341: Morph: A feature extraction toolset for spatial transcriptomics." Cancer Research 84, no. 6_Supplement (2024): 2341. http://dx.doi.org/10.1158/1538-7445.am2024-2341.

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Abstract Accurately defining spatial characteristics of tumors has been a challenge in cancer research. Specifically, there is still a lack of spatial transcriptomic (ST) bioinformatic methods that infer tumor boundaries, a necessity for tumor microenvironment (TME) analyses, that are fully automated and handle non-rectangular grids (like the one found in Visium). Here we introduce Morph, a toolset that not only addresses these limitations, but also accurately extracts tumor regions, layers surrounding them, and distances related to such regions. Morph was tested on a dataset composed of 117 S

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A., Salamov, and Grigoriev I. "Automatic Annotation of Mitochondrial Genomes in Fungi." Journal of Life Sciences and Biomedicine 67, no. 1 (2012): 20–24. https://doi.org/10.5281/zenodo.8352506.

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The sizeable fraction of fungal mitochondrial protein-coding genes contain introns of type I or (rarely) of type II, which presents a challenge for their correct prediction. We have developed the annotation pipeline, which for the first time allows the computational prediction of such types of genes. When tested on 82 genomes from GenBank, the algorithm has the accuracy of 91%/88% (sensitivity/specificity) at nucleotide level, and 84%/79% on the exon/ORF level.

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A., Salamov, and Grigoriev I. "Automatic Annotation of Mitochondrial Genomes in Fungi." Journal of Life Sciences and Biomedicine 67, no. 1 (2012): 20–24. https://doi.org/10.5281/zenodo.8362243.

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The sizeable fraction of fungal mitochondrial protein-coding genes contain introns of type I or (rarely) of type II, which presents a challenge for their correct prediction. We have developed the annotation pipeline, which for the first time allows the computational prediction of such types of genes. When tested on 82 genomes from GenBank, the algorithm has the accuracy of 91%/88% (sensitivity/specificity) at nucleotide level, and 84%/79% on the exon/ORF level.

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Harris, Alexandra R., Huaitian Liu, Brittany Jenkins-Lord, et al. "Abstract C044: Investigation of breast tumor biology and microenvironment in women of African descent using a single cell multiomic approach." Cancer Epidemiology, Biomarkers & Prevention 32, no. 12_Supplement (2023): C044. http://dx.doi.org/10.1158/1538-7755.disp23-c044.

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Abstract Women of African descent are at an increased risk of developing and dying from aggressive subtypes of breast cancer. A connection between aggressive disease and Western Sub-Saharan African ancestry has been postulated, but it remains largely unknown to what extent breast cancer in Africa is reminiscent of breast cancer in U.S. African American (AA) women who experience disproportionately high mortality rates. We performed ATAC- and RNA-sequencing on 9 human triple-negative breast cancer cell lines of U.S. origin and discovered that African ancestry influences the chromatin landscape,

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Li, Siyu, Songming Tang, Yunchang Wang, Sijie Li, Yuhang Jia, and Shengquan Chen. "Accurate cell type annotation for single‐cell chromatin accessibility data via contrastive learning and reference guidance." Quantitative Biology, February 8, 2024. http://dx.doi.org/10.1002/qub2.33.

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AbstractRecent advances in single‐cell chromatin accessibility sequencing (scCAS) technologies have resulted in new insights into the characterization of epigenomic heterogeneity and have increased the need for automatic cell type annotation. However, existing automatic annotation methods for scCAS data fail to incorporate the reference data and neglect novel cell types, which only exist in a test set. Here, we propose RAINBOW, a reference‐guided automatic annotation method based on the contrastive learning framework, which is capable of effectively identifying novel cell types in a test set.

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Dong, Sherry, Kaiwen Deng, and Xiuzhen Huang. "Single-Cell Type Annotation With Deep Learning in 265 Cell Types For Humans." Bioinformatics Advances, April 8, 2024. http://dx.doi.org/10.1093/bioadv/vbae054.

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Abstract Motivation Annotating cell types is a challenging yet essential task in analyzing single-cell RNA sequencing data. However, due to the lack of a gold standard, it is difficult to evaluate the algorithms fairly and an overfitting algorithm may be favored in benchmarks. To address this challenge, we developed a deep learning-based single-cell type prediction tool that assigns the cell type to 265 different cell types for humans, based on data from approximately five million cells. Results We achieved a median AUC of 0.93 when evaluated across datasets. We found that inconsistent labelin

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Hou, Wenpin, and Zhicheng Ji. "Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis." Nature Methods, March 25, 2024. http://dx.doi.org/10.1038/s41592-024-02235-4.

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AbstractHere we demonstrate that the large language model GPT-4 can accurately annotate cell types using marker gene information in single-cell RNA sequencing analysis. When evaluated across hundreds of tissue and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations. This capability can considerably reduce the effort and expertise required for cell type annotation. Additionally, we have developed an R software package GPTCelltype for GPT-4’s automated cell type annotation.

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Xu, Congmin, Huyun Lu, and Peng Qiu. "Comparison of cell type annotation algorithms for revealing immune response of COVID-19." Frontiers in Systems Biology 2 (October 24, 2022). http://dx.doi.org/10.3389/fsysb.2022.1026686.

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When analyzing scRNA-seq data with clustering algorithms, annotating the clusters with cell types is an essential step toward biological interpretation of the data. Annotations can be performed manually using known cell type marker genes. Annotations can also be automated using knowledge-driven or data-driven machine learning algorithms. Majority of cell type annotation algorithms are designed to predict cell types for individual cells in a new dataset. Since biological interpretation of scRNA-seq data is often made on cell clusters rather than individual cells, several algorithms have been de

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Busarello, Emma, Giulia Biancon, Ilaria Cimignolo, et al. "Cell Marker Accordion: interpretable single-cell and spatial omics annotation in health and disease." Nature Communications 16, no. 1 (2025). https://doi.org/10.1038/s41467-025-60900-4.

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Abstract Single-cell technologies offer a unique opportunity to explore cellular heterogeneity in health and disease. However, reliable identification of cell types and states represents a bottleneck. Available databases and analysis tools employ dissimilar markers, leading to inconsistent annotations and poor interpretability. Furthermore, current tools focus mostly on physiological cell types, limiting their applicability to disease. We present the Cell Marker Accordion, a user-friendly platform providing automatic annotation and unmatched biological interpretation of single-cell populations

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Qi, Qi, Yunhe Wang, Yujian Huang, Yi Fan, and Xiangtao Li. "PredGCN: A Pruning-enabled Gene-Cell Net for Automatic Cell Annotation of Single Cell Transcriptome Data." Bioinformatics, June 26, 2024. http://dx.doi.org/10.1093/bioinformatics/btae421.

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Abstract Motivation The annotation of cell types from single-cell transcriptomics is essential for understanding the biological identity and functionality of cellular populations. Although manual annotation remains the gold standard, the advent of automatic pipelines has become crucial for scalable, unbiased, and cost-effective annotations. Nonetheless, the effectiveness of these automatic methods, particularly those employing deep learning, significantly depends on the architecture of the classifier and the quality and diversity of the training datasets. Results To address these limitations,

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Theunissen, Lauren, Thomas Mortier, Yvan Saeys, and Willem Waegeman. "Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics." Briefings in Bioinformatics 26, no. 3 (2025). https://doi.org/10.1093/bib/bbaf239.

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Abstract Automatic cell-type annotation methods assign cell-type labels to new, unlabeled datasets by leveraging relationships from a reference RNA-seq atlas. However, new datasets may include labels absent from the reference dataset or exhibit feature distributions that diverge from it. These scenarios can significantly affect the reliability of cell type predictions, a factor often overlooked in current automatic annotation methods. The field of out-of-distribution detection (OOD), primarily focused on computer vision, addresses the identification of instances that differ from the training d

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Ceccarelli, Francesco, Pietro Liò, and Sean B. Holden. "AnnoGCD: a generalized category discovery framework for automatic cell type annotation." NAR Genomics and Bioinformatics 6, no. 4 (2024). https://doi.org/10.1093/nargab/lqae166.

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Abstract The identification of cell types in single-cell RNA sequencing (scRNA-seq) data is a critical task in understanding complex biological systems. Traditional supervised machine learning methods rely on large, well-labeled datasets, which are often impractical to obtain in open-world scenarios due to budget constraints and incomplete information. To address these challenges, we propose a novel computational framework, named AnnoGCD, building on Generalized Category Discovery (GCD) and Anomaly Detection (AD) for automatic cell type annotation. Our semi-supervised method combines labeled a

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