Relevant bibliographies by topics / Single cell multiomics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Single cell multiomics'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Single cell multiomics"

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Chappell, Lia, Andrew J. C. Russell, and Thierry Voet. "Single-Cell (Multi)omics Technologies." Annual Review of Genomics and Human Genetics 19, no. 1 (2018): 15–41. http://dx.doi.org/10.1146/annurev-genom-091416-035324.

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Single-cell multiomics technologies typically measure multiple types of molecule from the same individual cell, enabling more profound biological insight than can be inferred by analyzing each molecular layer from separate cells. These single-cell multiomics technologies can reveal cellular heterogeneity at multiple molecular layers within a population of cells and reveal how this variation is coupled or uncoupled between the captured omic layers. The data sets generated by these techniques have the potential to enable a deeper understanding of the key biological processes and mechanisms drivi
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Ingelfinger, Florian, Eduardo Beltrán, Lisa A. Gerdes, and Burkhard Becher. "Single-cell multiomics in neuroinflammation." Current Opinion in Immunology 76 (June 2022): 102180. http://dx.doi.org/10.1016/j.coi.2022.102180.

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Lee, Jeongwoo, Do Young Hyeon, and Daehee Hwang. "Single-cell multiomics: technologies and data analysis methods." Experimental & Molecular Medicine 52, no. 9 (2020): 1428–42. http://dx.doi.org/10.1038/s12276-020-0420-2.

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Abstract Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently, bulk multiomics analyses, such as multidimensional genomic and proteogenomic analyses, have proven beneficial for obtaining a comprehensive understanding of cellular events. This benefit has facilitated the development of single-cell multiomics analysis, which enables cell type-specific gene regulation to be examined. The cardinal features of single-cell multiomics analysis include (1) technologies for single-cell isol
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Demetci, Pinar, Rebecca Santorella, Björn Sandstede, William Stafford Noble, and Ritambhara Singh. "Single-Cell Multiomics Integration by SCOT." Journal of Computational Biology 29, no. 1 (2022): 19–22. http://dx.doi.org/10.1089/cmb.2021.0477.

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Macaulay, Iain C., Chris P. Ponting, and Thierry Voet. "Single-Cell Multiomics: Multiple Measurements from Single Cells." Trends in Genetics 33, no. 2 (2017): 155–68. http://dx.doi.org/10.1016/j.tig.2016.12.003.

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R Aroor, A. "Multiomics: Concepts, Methods and Applications." AJ Journal of Medical Sciences 1, no. 1 (2024): 12–15. https://doi.org/10.71325/ajjms.v1i1.arora.

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Multiomics is a high-throughput technology with multilayered system biology approach incorporating bioinformatic analysis of the data. At present it includes genomics, epigenomics, transcriptomics, proteomics, metabolomics, and gut microbiomics with advancement to the level of single cell analysis. Multiomics is an advancing approach (1) to understand the pathobiology of disease, (2) to identify sensitive biomarkers for the diagnosis of diseases as well as in monitoring and (3) to identify disease specific targets to treat the patient in the context of precision medicine as targeted therapeuti
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Nassar, Sam F., Khadir Raddassi, and Terence Wu. "Single-Cell Multiomics Analysis for Drug Discovery." Metabolites 11, no. 11 (2021): 729. http://dx.doi.org/10.3390/metabo11110729.

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Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological propertie
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Ak, Cigdem, Nicole Szczepanski, Aaron Doe, and Gurkan Yardimci. "Abstract 5001: Multimodal topic modeling for single-cell multiomics." Cancer Research 85, no. 8_Supplement_1 (2025): 5001. https://doi.org/10.1158/1538-7445.am2025-5001.

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Abstract Single-cell (sc) multiomics assays simultaneously measure transcriptomic, epigenomic, and proteomic modalities in single cells. These multimodal assays enable deeper characterization of cells compared to single-omic assays by offering a more comprehensive measurement of the cell state. Additionally, they can identify cross modality associations, such as promoter-enhancer linkages. Recently, numerous computational methods for sc-multiomics data have been released; however, there is a dearth of interpretable methods While matrix factorization and deep learning approaches, such as MOFA+
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Blutt, Sarah E., Cristian Coarfa, Josef Neu, and Mohan Pammi. "Multiomic Investigations into Lung Health and Disease." Microorganisms 11, no. 8 (2023): 2116. http://dx.doi.org/10.3390/microorganisms11082116.

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Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics inv
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Perkel, Jeffrey M. "Single-cell analysis enters the multiomics age." Nature 595, no. 7868 (2021): 614–16. http://dx.doi.org/10.1038/d41586-021-01994-w.

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Dissertations / Theses on the topic "Single cell multiomics"

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CAPRIOLI, CHIARA. "INTEGRATED SINGLE-CELL MUTATION, GENE EXPRESSION AND ISOFORM ANALYSIS TO DECONVOLVE ACUTE MYELOID LEUKEMIA HETEROGENEITY." Doctoral thesis, Università degli Studi di Milano, 2022. https://hdl.handle.net/2434/946325.

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Acute myeloid leukemia (AML) is an aggressive cancer arising from the hematopoietic stem cell (HSC). As other tumor types, AMLs are characterized by multiple and interconnected levels of intra-tumor heterogeneity, including genetic (DNA mutations), phenotypic (transcriptional patterns) and ecological (interactions with host immune-cells) diversity. Emerging evidence suggest that intra-tumor heterogeneity impacts directly on leukemogenesis, disease prognosis and sensitivity/resistance to available treatments. How the different layers of intra-tumoral heterogeneity interact with each other and s
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Book chapters on the topic "Single cell multiomics"

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Ramsden, Jeremy. "Single Cell Analysis and Multiomics." In Computational Biology. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-45607-8_21.

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Sambath, Janani, Krishna Patel, Sewanti Limaye, and Prashant Kumar. "Single-Cell Multiomics: Dissecting Cancer." In Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2445-5_14.

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Xu, Yungang, and Xiaobo Zhou. "Applications of Single-Cell Sequencing for Multiomics." In Methods in Molecular Biology. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7717-8_19.

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Hassan, Muhammad Jawad, Muhammad Faheem, and Sabba Mehmood. "Emerging OMICS and Genetic Disease." In Omics Technologies for Clinical Diagnosis and Gene Therapy: Medical Applications in Human Genetics. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815079517122010010.

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Multiomics also described as integrative omics is an analytical approach that combines data from multiple ‘omics’ approaches including genomics, transcriptomics, proteomics, metabolomics, epigenomics, metagenomics and Meta transcriptomics to answer the complex biological processes involved in rare genetic disorders. This omics approach is particularly helpful since it identifies biomarkers of disease progression and treatment progress by collective characterization and quantification of pools of biological molecules within and among the various types of cells to better understand and categoriz
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Conference papers on the topic "Single cell multiomics"

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Tao, Huayu, Xinliang Sun, Yuxuan Chen, Min Li, and Ruiqing Zheng. "scGDCC: Graph-based Dual Contrastive Calibration for Single Cell MultiOmics Clustering." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822576.

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Li, Bingjun, and Sheida Nabavi. "Contrastive Learning in Single-cell Multiomics Clustering." In BCB '23: 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM, 2023. http://dx.doi.org/10.1145/3584371.3613010.

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dong, hao, Wei Kong, Wenliang Gao, and Zhong Chen. "An accurate clustering algorithm for single-cell multiomics data." In 3rd International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), edited by Zulqarnain Baloch and Pier Paolo Piccaluga. SPIE, 2024. http://dx.doi.org/10.1117/12.3036648.

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Li, Bingjun, and Sheida Nabavi. "scGEMOC, A Graph Embedded Contrastive Learning Single-cell Multiomics Clustering Model." In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2023. http://dx.doi.org/10.1109/bibm58861.2023.10385267.

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Koca, Mehmet Burak, and Fatih Erdoğan Sevilgen. "Comparative Analysis of Fusion Techniques for Integrating Single-cell Multiomics Datasets." In 2024 32nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2024. http://dx.doi.org/10.1109/siu61531.2024.10601063.

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Hu, X., B. Minasenko, D. Wang, et al. "Single-cell Multiomics Identifies Impact of Prenatal Heavy Metal Exposure on Airway Development." In American Thoracic Society 2024 International Conference, May 17-22, 2024 - San Diego, CA. American Thoracic Society, 2024. http://dx.doi.org/10.1164/ajrccm-conference.2024.209.1_meetingabstracts.a6953.

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Kara, Nihan, Xiaoshan Shi, Nikolay Samusik, Stephanie Widmann, and Aaron J. Tyznik. "Abstract 4497: A single-cell multiomics approach to study tumor-driven perturbations during hematopoiesis in mice." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-4497.

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Shi, Xiaoshan, Margaret Nakamoto, Aaron Middlebrook, et al. "Abstract 2788: Deep characterization of tumor infiltrating leukocytes using a combination of flow cytometry and single-cell multiomics." In Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-2788.

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Carl, Sarah, Juana Flores-Candia, Jeremy Staub, et al. "1438 Integrative analysis of single cell multiomics data using deep learning to identify immune related biomarkers in a patient derived 3D ex vivo tumoroid platform." In SITC 37th Annual Meeting (SITC 2022) Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/jitc-2022-sitc2022.1438.

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Corselli, Mirko, Suraj Saksena, Margaret Nakamoto, Woodrow E. Lomas, Ian Taylor, and Pratip K. Chattopadhyay. "Abstract 2166: Deep characterization ofin vitrochronically stimulated T cells via single-cell multiomic analysis." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-2166.

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Reports on the topic "Single cell multiomics"

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Pasa Tolic, Ljiljana. High throughput single cell multiomics platform - CRADA 561 (Abstract). Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1909570.

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Kevin Shen, Kevin Shen. Single-Cell Multiomic Profiling of Killifish for Cell-Cultured Seafood. Experiment, 2023. http://dx.doi.org/10.18258/56360.

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