Relevant bibliographies by topics / Single-Cell omics

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

Academic literature on the topic 'Single-Cell omics'

Author: Grafiati
Published: 1 February 2025
Last updated: 31 July 2025

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Journal articles on the topic "Single-Cell omics"

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Choi, Joung Min, Chaelin Park, and Heejoon Chae. "moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud." PeerJ 12 (February 26, 2024): e17006. http://dx.doi.org/10.7717/peerj.17006.

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Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencin
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Rusk, Nicole. "Multi-omics single-cell analysis." Nature Methods 16, no. 8 (2019): 679. http://dx.doi.org/10.1038/s41592-019-0519-3.

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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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Xu, Xing, Junxia Wang, Lingling Wu, et al. "Microfluidic Single‐Cell Omics Analysis." Small 16, no. 9 (2019): 1903905. http://dx.doi.org/10.1002/smll.201903905.

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Wang, Le, and Bo Jin. "Single-Cell RNA Sequencing and Combinatorial Approaches for Understanding Heart Biology and Disease." Biology 13, no. 10 (2024): 783. http://dx.doi.org/10.3390/biology13100783.

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By directly measuring multiple molecular features in hundreds to millions of single cells, single-cell techniques allow for comprehensive characterization of the diversity of cells in the heart. These single-cell transcriptome and multi-omic studies are transforming our understanding of heart development and disease. Compared with single-dimensional inspections, the combination of transcriptomes with spatial dimensions and other omics can provide a comprehensive understanding of single-cell functions, microenvironment, dynamic processes, and their interrelationships. In this review, we will in
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Mincarelli, Laura, Ashleigh Lister, James Lipscombe, and Iain C. Macaulay. "Defining Cell Identity with Single-Cell Omics." PROTEOMICS 18, no. 18 (2018): 1700312. http://dx.doi.org/10.1002/pmic.201700312.

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Yang, Xiaoxi, Yuqi Wen, Xinyu Song, Song He, and Xiaochen Bo. "Exploring the classification of cancer cell lines from multiple omic views." PeerJ 8 (August 18, 2020): e9440. http://dx.doi.org/10.7717/peerj.9440.

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Background Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial. Methods In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, mi
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Deng, Yanxiang, Amanda Finck, and Rong Fan. "Single-Cell Omics Analyses Enabled by Microchip Technologies." Annual Review of Biomedical Engineering 21, no. 1 (2019): 365–93. http://dx.doi.org/10.1146/annurev-bioeng-060418-052538.

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Single-cell omics studies provide unique information regarding cellular heterogeneity at various levels of the molecular biology central dogma. This knowledge facilitates a deeper understanding of how underlying molecular and architectural changes alter cell behavior, development, and disease processes. The emerging microchip-based tools for single-cell omics analysis are enabling the evaluation of cellular omics with high throughput, improved sensitivity, and reduced cost. We review state-of-the-art microchip platforms for profiling genomics, epigenomics, transcriptomics, proteomics, metabolo
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Guan, Angel, and Camelia Quek. "Single-Cell Multi-Omics: Insights into Therapeutic Innovations to Advance Treatment in Cancer." International Journal of Molecular Sciences 26, no. 6 (2025): 2447. https://doi.org/10.3390/ijms26062447.

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Advances in single-cell multi-omics technologies have deepened our understanding of cancer biology by integrating genomic, transcriptomic, epigenomic, and proteomic data at single-cell resolution. These single-cell multi-omics technologies provide unprecedented insights into tumour heterogeneity, tumour microenvironment, and mechanisms of therapeutic resistance, enabling the development of precision medicine strategies. The emerging field of single-cell multi-omics in genomic medicine has improved patient outcomes. However, most clinical applications still depend on bulk genomic approaches, wh
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Lan, Wei, Tongsheng Ling, Qingfeng Chen, Ruiqing Zheng, Min Li, and Yi Pan. "scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis." PLOS Computational Biology 20, no. 12 (2024): e1012679. https://doi.org/10.1371/journal.pcbi.1012679.

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With the rapidly development of biotechnology, it is now possible to obtain single-cell multi-omics data in the same cell. However, how to integrate and analyze these single-cell multi-omics data remains a great challenge. Herein, we introduce an interpretable multitask framework (scMoMtF) for comprehensively analyzing single-cell multi-omics data. The scMoMtF can simultaneously solve multiple key tasks of single-cell multi-omics data including dimension reduction, cell classification and data simulation. The experimental results shows that scMoMtF outperforms current state-of-the-art algorith
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Dissertations / Theses on the topic "Single-Cell omics"

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Kim, Jieun. "Computational tools for the integrative analysis of muti-omics data to decipher trans-omics networks." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28524.

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Regulatory networks define the phenotype, morphology, and function of cells. These networks are built from the basic building blocks of the cell—DNA, RNA, and proteins—and cut across the respective omics layers—genome, transcriptome, and proteome. The resulting omics networks depict a near infinite possibility of nodes and edges that intricately connect the ‘omes’. With the rapid advancement in the technologies that generate omics data in bulk samples and now at single-cell resolution, the field of life sciences is now met with the challenge to connect these omes to generate trans-omics networ
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Lin, Yingxin. "Statistical modelling and machine learning for single cell data harmonisation and analysis." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28034.

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Technological advances such as large-scale single-cell profiling have exploded in recent years and enabled unprecedented understanding of the behaviour of individual cells. Effectively harmonising multiple collections and different modalities of single-cell data and accurately annotating cell types using reference, which we consider as the step of “intermediate data analysis” in this thesis, serve as a foundation for the downstream analysis to uncover biological insights from single-cell data. This thesis proposed several statistical modelling and machine learning methods to address several ch
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Kim, Taiyun. "Development of statistical methods for integrative omics analysis in precision medicine." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28838.

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Precision medicine is an integrative approach to the prevention and treatment of complex diseases such as cardiovascular disease that considers an individual’s lifestyle, clinical information, and omics profile. In the last decade, the advances in omics technologies have allowed researchers to gain insight into biological systems and progress to precision medicine. Many omics technology now enables us to rapidly generate, store and analyse data at a large scale. Many efforts have attempted to integrate large-scale multi-batch and multi-omics data. While many strategies have been developed, cha
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Dupuis, Louise. "Causal network analysis of single cell multi-omics data applied to a cellular therapy against multiple sclerosis." Electronic Thesis or Diss., Sorbonne université, 2024. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2024SORUS628.pdf.

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Les travaux de cette thèse se situent à l'intersection de la découverte causale, de l'analyse de données single-cell multi-omiques et de l'immunologie, en particulier de la sclérose en plaques. Son objectif principal est d'adapter et d'étendre les algorithmes de découverte causale tels que MIIC (Multivariate Information-base Inductive Causation) qui reconstruisent les réseaux causaux à partir de données d'observation pour les adapter aux données single-cell multi-omiques, et de les appliquer à l'étude des mécanismes sous-jacents à une thérapie potentielle contre la sclérose en plaques. Cette r
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Blampey, Quentin. "Deep learning and computational methods on single-cell and spatial data for precision medicine in oncology." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASL116.

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La médecine de précision en oncologie a pour but de personnaliser les traitements en fonction des profils génétiques et moléculaires uniques des tumeurs des patients, et ce afin d'améliorer l'efficacité thérapeutique ou de minimiser les effets secondaires. À mesure que les avancées technologiques produisent des données de plus en plus précises sur le microenvironnement tumoral (TME), la complexité de ces données augmente également. Notamment, les données spatiales — un type récent et prometteur de données omiques — fournissent des informations moléculaires à la résolution de la cellule tout en
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Ronen, Jonathan. "Integrative analysis of data from multiple experiments." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21612.

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Auf die Entwicklung der Hochdurchsatz-Sequenzierung (HTS) folgte eine Reihe von speziellen Erweiterungen, die erlauben verschiedene zellbiologischer Aspekte wie Genexpression, DNA-Methylierung, etc. zu messen. Die Analyse dieser Daten erfordert die Entwicklung von Algorithmen, die einzelne Experimenteberücksichtigen oder mehrere Datenquellen gleichzeitig in betracht nehmen. Der letztere Ansatz bietet besondere Vorteile bei Analyse von einzelligen RNA-Sequenzierung (scRNA-seq) Experimenten welche von besonders hohem technischen Rauschen, etwa durch den Verlust an Molekülen durch die Behandlung
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Czerwińska, Urszula. "Unsupervised deconvolution of bulk omics profiles : methodology and application to characterize the immune landscape in tumors Determining the optimal number of independent components for reproducible transcriptomic data analysis Application of independent component analysis to tumor transcriptomes reveals specific and reproducible immune-related signals A multiscale signalling network map of innate immune response in cancer reveals signatures of cell heterogeneity and functional polarization." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCB075.

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Les tumeurs sont entourées d'un microenvironnement complexe comprenant des cellules tumorales, des fibroblastes et une diversité de cellules immunitaires. Avec le développement actuel des immunothérapies, la compréhension de la composition du microenvironnement tumoral est d'une importance critique pour effectuer un pronostic sur la progression tumorale et sa réponse au traitement. Cependant, nous manquons d'approches quantitatives fiables et validées pour caractériser le microenvironnement tumoral, facilitant ainsi le choix de la meilleure thérapie. Une partie de ce défi consiste à quantifier
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CAPORALE, NICOLO'. "A UNIFYING FRAMEWORK TO STUDY THE GENETIC AND ENVIRONMENTAL FACTORS SHAPING HUMAN BRAIN DEVELOPMENT." Doctoral thesis, Università degli Studi di Milano, 2020. http://hdl.handle.net/2434/697871.

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The development of human brain is a fascinating and complex process that still needs to be uncovered at the molecular resolution. Even though animal studies have revealed a lot of its unfolding, the fine regulation of cellular differentiation trajectories that characterizes humans has become only recently open to experimental tractability, thanks to the development of organoids, human cellular models that are able to recapitulate the spatiotemporal architecture of the brain in a 3D fashion. Here we first benchmarked human brain organoids at the level of transcriptomic and structural architectu
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Books on the topic "Single-Cell omics"

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Sweedler, Jonathan V., James Eberwine, and Scott E. Fraser, eds. Single Cell ‘Omics of Neuronal Cells. Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2525-5.

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Single-Cell Omics. Elsevier, 2019. http://dx.doi.org/10.1016/c2017-0-02420-5.

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Single-Cell Omics. Elsevier, 2019. http://dx.doi.org/10.1016/c2018-0-02201-x.

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Pan, Xinghua, Shixiu Wu, and Sherman M. Weissman, eds. Introduction to Single Cell Omics. Frontiers Media SA, 2019. http://dx.doi.org/10.3389/978-2-88945-920-9.

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Eberwine, James, Jonathan V. Sweedler, and Scott E. Fraser. Single Cell 'Omics of Neuronal Cells. Springer, 2022.

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Single Cell 'Omics of Neuronal Cells. Springer, 2023.

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Barh, Debmalya, and Vasco Azevedo. Single-Cell Omics: Technological Advances and Applications. Elsevier Science & Technology, 2019.

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Menon, Swapna. Single Cell Sequencing Essentials in Brief: Single Cell RNA Sequencing and Orthogonal Omics Technologies. Independently Published, 2021.

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Barh, Debmalya, and Vasco Azevedo. Single-Cell Omics : Volume 2: Technological Advances and Applications. Elsevier Science & Technology Books, 2019.

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Barh, Debmalya, and Vasco Azevedo. Single-Cell Omics : Volume 1: Technological Advances and Applications. Elsevier Science & Technology, 2019.

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Book chapters on the topic "Single-Cell omics"

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Lynch, Mark, and Naveen Ramalingam. "Integrated Fluidic Circuits for Single-Cell Omics and Multi-omics Applications." In Single Molecule and Single Cell Sequencing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6037-4_2.

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Wang, Jingshu, and Tianyu Chen. "Deep Learning Methods for Single-Cell Omics Data." In Springer Handbooks of Computational Statistics. Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65902-1_6.

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Wang, Xinjun, Haoran Hu, and Wei Chen. "Model-Based Clustering of Single-Cell Omics Data." In Springer Handbooks of Computational Statistics. Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65902-1_5.

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Yuan, Yuzhuo, Yang Jin, Xin Wang, Ping Wang, and Daohui Ge. "SMTFusion: Multi-order Topological Cell Graphs for Single-Cell Multi-omics Clustering." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-95-0027-7_43.

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Demetçi, Pınar, Rebecca Santorella, Björn Sandstede, and Ritambhara Singh. "Unsupervised Integration of Single-Cell Multi-omics Datasets with Disproportionate Cell-Type Representation." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04749-7_1.

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Han, Maozhen, Pengshuo Yang, Hao Zhou, Hongjun Li, and Kang Ning. "Metagenomics and Single-Cell Omics Data Analysis for Human Microbiome Research." In Advances in Experimental Medicine and Biology. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1503-8_6.

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Chau, Tran, Prakash Timilsena, and Song Li. "Gene Regulatory Network Modeling Using Single-Cell Multi-Omics in Plants." In Methods in Molecular Biology. Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3354-0_16.

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Li, Yue, Gregory Fonseca, and Jun Ding. "Multimodal Methods for Knowledge Discovery from Bulk and Single-Cell Multi-Omics Data." In Machine Learning Methods for Multi-Omics Data Integration. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36502-7_4.

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Misra, Biswapriya B. "A Workflow in Single Cell-Type Metabolomics: From Data Pre-Processing and Statistical Analysis to Biological Insights." In OMICS-Based Approaches in Plant Biotechnology. John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119509967.ch6.

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Srivastava, Upasna, Sonal Sukreet, Swarna Kanchan, Minu Kesheri, and Manish Kumar Gupta. "Multiresolution Insights into Single-Cell Landscapes: Integrating Genomics, Epigenomics, and Proteomics for Brain Studies." In Multi-Omics in Biomedical Sciences and Environmental Sustainability. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-7067-3_4.

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Conference papers on the topic "Single-Cell omics"

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Haase, Christa, Qi Yu, Debra Van Egeren, et al. "Next-Generation Image-Guided Single-Cell ‘Omics Analysis." In Optical Tomography and Spectroscopy. Optica Publishing Group, 2024. https://doi.org/10.1364/ots.2024.om1d.1.

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To discover how cellular organization and dynamics direct tissue function, we developed a technology for image-guided single cell transcriptional analysis. We present its application to stem cell and leukemia biology and discuss future technological advancements. Full-text article not available; see video presentation
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Li, Qi, Jian-Wei Su, and Wen-Hui Wu. "Clustering Single-Cell Multi-Omics Data with Graph Contrastive Learning." In 2024 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2024. https://doi.org/10.1109/icmlc63072.2024.10935198.

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Li, Xiaoli, Rui Zhang, Saba Aslam, et al. "scMonica: Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822866.

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Taha, Manar H., Mohamed El-Hadidi, and Sahar Ali Fawzi. "Deep Learning Applications in Single-Cell Multi-Omics Analysis: A Review." In 2024 6th Novel Intelligent and Leading Emerging Sciences Conference (NILES). IEEE, 2024. http://dx.doi.org/10.1109/niles63360.2024.10753202.

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Pang, Shanchen, Jiarui Wu, Wenhao Wu, et al. "scKADE: Single-Cell Multi-Omics Integration with Kolmogorov-Arnold Deep Embedding." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822086.

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Li, Jiawei, Shizhan Chen, Zongbo Han, Wei Li, Jijun Tang, and Fei Guo. "Multi-Task Driven Multi-Level Dynamical Fusion for Single-Cell Multi-Omics Cell Type Annotation." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822524.

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Li, Enling, Lin Gao, and Yusen Ye. "CellFeature: Cell and Feature Co-Embedding from Single-Cell Multi-Omics with Heterogeneous Graph Model." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821837.

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Wen, Ke-Pei, Ying Li, and Le Ou-Yang. "SCDMSC: Deep Multi-View Subspace Clustering for Single-Cell Multi-Omics Data." In 2024 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2024. https://doi.org/10.1109/icmlc63072.2024.10935051.

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Li, Xiong, Jiapeng Zhao, Longyu Zhang, and Juan Zhou. "A gene regulatory network method based on single-cell multi-omics data." In 2024 6th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI). IEEE, 2024. https://doi.org/10.1109/ricai64321.2024.10911259.

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Wolfgang, Seth, Skyler Ruiter, Marc Tunnell, Timothy Triche, Erin Carrier, and Zachary DeBruine. "Value-Compressed Sparse Column (VCSC): Sparse Matrix Storage for Single-cell Omics Data." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825091.

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Reports on the topic "Single-Cell omics"

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Wang, Daojing, and Steven Bodovitz. Single cell analysis: the new frontier in 'Omics'. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/983315.

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