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Artículos de revistas sobre el tema "Single-Cell omics"

Autor: Grafiati

Publicado: 1 de febrero de 2025

Última modificación: 31 de julio de 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Rai, Muhammad Farooq, Chia-Lung Wu, Terence D. Capellini, et al. "Single Cell Omics for Musculoskeletal Research." Current Osteoporosis Reports 19, no. 2 (2021): 131–40. http://dx.doi.org/10.1007/s11914-021-00662-2.

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12

Lv, Dekang, Xuehong Zhang, and Quentin Liu. "Single-cell omics decipher tumor evolution." Medicine in Omics 2 (September 2021): 100006. http://dx.doi.org/10.1016/j.meomic.2021.100006.

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13

Colomé-Tatché, M., and F. J. Theis. "Statistical single cell multi-omics integration." Current Opinion in Systems Biology 7 (February 2018): 54–59. http://dx.doi.org/10.1016/j.coisb.2018.01.003.

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14

Kodzius, Rimantas, and Takashi Gojobori. "Single-cell technologies in environmental omics." Gene 576, no. 2 (2016): 701–7. http://dx.doi.org/10.1016/j.gene.2015.10.031.

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15

Chen, Jiani, Wanzi Xiao, Eric Zhang, and Xiang Chen. "Abstract 4943: Benchmarking unpaired single-cell RNA and single-cell ATAC integration." Cancer Research 84, no. 6_Supplement (2024): 4943. http://dx.doi.org/10.1158/1538-7445.am2024-4943.

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Abstract The integration of single-cell RNA-sequencing (scRNA-seq) and single-cell ATAC-sequencing (scATAC-seq) data offers a unique opportunity to gain a comprehensive view of cellular identity with defining features and to infer gene-regulatory relationships. Despite the emergence of technologies that simultaneously capture both the gene expression and chromatin accessibility of individual cells (paired data), the practical challenges of these approaches (e.g., the unavailability in previous samples and prohibitive cost) have led researchers to turn to the existing trove of single-modality d

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16

Moarefian, Maryam, Antonia McDonnell Capossela, Ryan Eom, and Kiana Aran. "Single-Cell Technologies: Advances in Single-Cell Migration and Multi-Omics." GEN Biotechnology 1, no. 3 (2022): 246–61. http://dx.doi.org/10.1089/genbio.2022.0014.

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17

Sun, Yueqiu, Nianzuo Yu, Junhu Zhang, and Bai Yang. "Advances in Microfluidic Single-Cell RNA Sequencing and Spatial Transcriptomics." Micromachines 16, no. 4 (2025): 426. https://doi.org/10.3390/mi16040426.

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The development of micro- and nano-fabrication technologies has greatly advanced single-cell and spatial omics technologies. With the advantages of integration and compartmentalization, microfluidic chips are capable of generating high-throughput parallel reaction systems for single-cell screening and analysis. As omics technologies improve, microfluidic chips can now integrate promising transcriptomics technologies, providing new insights from molecular characterization for tissue gene expression profiles and further revealing the static and even dynamic processes of tissues in homeostasis an

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18

Bai, Dongsheng, Jinying Peng, and Chengqi Yi. "Advances in single-cell multi-omics profiling." RSC Chemical Biology 2, no. 2 (2021): 441–49. http://dx.doi.org/10.1039/d0cb00163e.

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19

Ji, Yuge, Mohammad Lotfollahi, F. Alexander Wolf, and Fabian J. Theis. "Machine learning for perturbational single-cell omics." Cell Systems 12, no. 6 (2021): 522–37. http://dx.doi.org/10.1016/j.cels.2021.05.016.

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20

Marx, Vivien. "How single-cell multi-omics builds relationships." Nature Methods 19, no. 2 (2022): 142–46. http://dx.doi.org/10.1038/s41592-022-01392-8.

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21

Stein, Richard A. "Single-Cell Sequencing Sifts through Multiple Omics." Genetic Engineering & Biotechnology News 39, no. 7 (2019): 32–36. http://dx.doi.org/10.1089/gen.39.07.10.

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22

Song, Yanling, Xing Xu, Wei Wang, Tian Tian, Zhi Zhu, and Chaoyong Yang. "Single cell transcriptomics: moving towards multi-omics." Analyst 144, no. 10 (2019): 3172–89. http://dx.doi.org/10.1039/c8an01852a.

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23

Slavov, Nikolai. "Unlocking the Potential of Single-Cell Omics." Journal of Proteome Research 24, no. 4 (2025): 1481. https://doi.org/10.1021/acs.jproteome.5c00197.

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24

Dong, Xianjun, Chunyu Liu, and Mikhail Dozmorov. "Review of multi-omics data resources and integrative analysis for human brain disorders." Briefings in Functional Genomics 20, no. 4 (2021): 223–34. http://dx.doi.org/10.1093/bfgp/elab024.

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Abstract In the last decade, massive omics datasets have been generated for human brain research. It is evolving so fast that a timely update is urgently needed. In this review, we summarize the main multi-omics data resources for the human brains of both healthy controls and neuropsychiatric disorders, including schizophrenia, autism, bipolar disorder, Alzheimer’s disease, Parkinson’s disease, progressive supranuclear palsy, etc. We also review the recent development of single-cell omics in brain research, such as single-nucleus RNA-seq, single-cell ATAC-seq and spatial transcriptomics. We fu

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25

Weiskittel, Taylor M., Cristina Correia, Grace T. Yu, et al. "The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches." Genes 12, no. 7 (2021): 1098. http://dx.doi.org/10.3390/genes12071098.

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Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this revie

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26

Leenders, Floris, Eelco J. P. de Koning та Françoise Carlotti. "Pancreatic β-Cell Identity Change through the Lens of Single-Cell Omics Research". International Journal of Molecular Sciences 25, № 9 (2024): 4720. http://dx.doi.org/10.3390/ijms25094720.

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The main hallmark in the development of both type 1 and type 2 diabetes is a decline in functional β-cell mass. This decline is predominantly attributed to β-cell death, although recent findings suggest that the loss of β-cell identity may also contribute to β-cell dysfunction. This phenomenon is characterized by a reduced expression of key markers associated with β-cell identity. This review delves into the insights gained from single-cell omics research specifically focused on β-cell identity. It highlights how single-cell omics based studies have uncovered an unexpected level of heterogenei

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27

Cao, Kai, Xiangqi Bai, Yiguang Hong, and Lin Wan. "Unsupervised topological alignment for single-cell multi-omics integration." Bioinformatics 36, Supplement_1 (2020): i48—i56. http://dx.doi.org/10.1093/bioinformatics/btaa443.

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Abstract Motivation Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging. Results In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset int

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28

Loscalzo, Joseph. "Multi-Omics and Single-Cell Omics: New Tools in Drug Target Discovery." Arteriosclerosis, Thrombosis, and Vascular Biology 44, no. 4 (2024): 759–62. http://dx.doi.org/10.1161/atvbaha.124.320686.

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29

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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30

Rybakov, M. A., N. A. Omelyanchuk, and E. V. Zemlyanskaya. "Reconstruction of gene regulatory networks from single cell transcriptomic data." Vavilov Journal of Genetics and Breeding 28, no. 8 (2025): 974–81. https://doi.org/10.18699/vjgb-24-104.

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Gene regulatory networks (GRNs) – interpretable graph models of gene expression regulation – are a pivotal tool for understanding and investigating the mechanisms utilized by cells during development and in response to various internal and external stimuli. Historically, the first approach for the GRN reconstruction was based on the analysis of published data (including those summarized in databases). Currently, the primary GRN inference approach is the analysis of omics (mainly transcriptomic) data; a number of mathematical methods have been adapted for that. Obtaining omics data for individu

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31

Hsieh, James J., Natalia Miheecheva, Akshaya Ramachandran, et al. "Integrated single-cell spatial multi-omics of intratumor heterogeneity in renal cell carcinoma." Journal of Clinical Oncology 38, no. 15_suppl (2020): e17106-e17106. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e17106.

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e17106 Background: Clear cell renal cell carcinoma (ccRCC) exhibits conspicuous intratumor heterogeneity (ITH) - a driver of tumor evolution and metastasis. ITH in RCC has been studied extensively with bulk tumor DNA sequencing, which lacks the ability to integrate single cell resolution data, spatial architecture, and microenvironment composition. Therefore, we analyzed primary ccRCC tumors at multiple biopsy sites with CyTOF, multiplex immunofluorescence (MxIF), whole exome sequencing (WES), RNA sequencing (RNA-seq), single nuclei RNA-seq (snRNA-seq), and whole genome bisulfite sequencing (W

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32

Basso, K. "SINGLE CELL OMICS IN THE STUDY OF B CELL LYMPHOMA." Hematological Oncology 41, S2 (2023): 37. http://dx.doi.org/10.1002/hon.3163_7.

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33

Adossa, Nigatu, Sofia Khan, Kalle T. Rytkönen, and Laura L. Elo. "Computational strategies for single-cell multi-omics integration." Computational and Structural Biotechnology Journal 19 (2021): 2588–96. http://dx.doi.org/10.1016/j.csbj.2021.04.060.

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34

Czarnewski, Paulo, Ahmed Mahfouz, Raffaele A. Calogero, et al. "Community-driven ELIXIR activities in single-cell omics." F1000Research 11 (July 29, 2022): 869. http://dx.doi.org/10.12688/f1000research.122312.1.

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Single-cell omics (SCO) has revolutionized the way and the level of resolution by which life science research is conducted, not only impacting our understanding of fundamental cell biology but also providing novel solutions in cutting-edge medical research. The rapid development of single-cell technologies has been accompanied by the active development of data analysis methods, resulting in a plethora of new analysis tools and strategies every year. Such a rapid development of SCO methods and tools poses several challenges in standardization, benchmarking, computational resources and training.

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35

Tang, Lin. "Arsenal of single-cell multi-omics methods expanded." Nature Methods 18, no. 8 (2021): 858. http://dx.doi.org/10.1038/s41592-021-01245-w.

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36

Pan, Lu, Paolo Parini, Roman Tremmel, et al. "Single Cell Atlas: a single-cell multi-omics human cell encyclopedia." Genome Biology 25, no. 1 (2024). http://dx.doi.org/10.1186/s13059-024-03246-2.

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AbstractSingle-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult t

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37

Theunis, Koen, Sebastiaan Vanuytven, Irene Claes, et al. "Single-cell genome and transcriptome sequencing without upfront whole-genome amplification reveals cell state plasticity of melanoma subclones." Nucleic Acids Research 53, no. 6 (2025). https://doi.org/10.1093/nar/gkaf173.

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Abstract Single-cell multi-omics methods enable the study of cell state diversity, which is largely determined by the interplay of the genome, epigenome, and transcriptome. Here, we describe Gtag&T-seq, a genome-and-transcriptome sequencing (G&T-seq) protocol of the same single cells that omits whole-genome amplification (WGA) by using direct genomic tagmentation (Gtag). Gtag drastically decreases the cost and improves coverage uniformity at single-cell and pseudo-bulk levels compared to WGA-based G&T-seq. We also show that transcriptome-based DNA copy number inference has limited

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38

Xu, Jing, De‐Shuang Huang, and Xiujun Zhang. "scmFormer Integrates Large‐Scale Single‐Cell Proteomics and Transcriptomics Data by Multi‐Task Transformer." Advanced Science, March 14, 2024. http://dx.doi.org/10.1002/advs.202307835.

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AbstractTransformer‐based models have revolutionized single cell RNA‐seq (scRNA‐seq) data analysis. However, their applicability is challenged by the complexity and scale of single‐cell multi‐omics data. Here a novel single‐cell multi‐modal/multi‐task transformer (scmFormer) is proposed to fill up the existing blank of integrating single‐cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large‐scale single‐cell multimodal data and heterogeneous multi‐batch paired multi‐omics data, while preserving shared information a

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39

Chen, Fuqun, Guanhua Zou, Yongxian Wu, and Le Ou-Yang. "Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning." Bioinformatics, March 28, 2024. http://dx.doi.org/10.1093/bioinformatics/btae169.

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Abstract Motivation Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-cell RNA sequencing techniques to identify cell clusters, the information contained in mono-omic data is often limited, leading to suboptimal clustering performance. The emergence of single-cell multi-omics sequencing technologies enables the integration of multiple omics data for identifying cell clusters, but how to integrate different om

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40

Scala, Giovanni, Luigi Ferraro, Aurora Brandi, Yan Guo, Barbara Majello, and Michele Ceccarelli. "MoNETA: MultiOmics Network Embedding for SubType Analysis." NAR Genomics and Bioinformatics 6, no. 4 (2024). http://dx.doi.org/10.1093/nargab/lqae141.

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Abstract Cells are complex systems whose behavior emerges from a huge number of reactions taking place within and among different molecular districts. The availability of bulk and single-cell omics data fueled the creation of multi-omics systems biology models capturing the dynamics within and between omics layers. Powerful modeling strategies are needed to cope with the increased amount of data to be interrogated and the relative research questions. Here, we present MultiOmics Network Embedding for SubType Analysis (MoNETA) for fast and scalable identification of relevant multi-omics relation

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41

Yuan, Musu, Liang Chen, and Minghua Deng. "Clustering single-cell multi-omics data with MoClust." Bioinformatics, November 16, 2022. http://dx.doi.org/10.1093/bioinformatics/btac736.

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Abstract Motivation Single-cell multi-omics sequencing techniques have rapidly developed in the past few years. Clustering analysis with single-cell multi-omics data may give us novel perspectives to dissect cellular heterogeneity. However, multi-omics data have the properties of inherited large dimension, high sparsity and existence of doublets. Moreover, representations of different omics from even the same cell follow diverse distributions. Without proper distribution alignment techniques, clustering methods will encounter less separable clusters easily affected by less informative omics da

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42

Liu, Yufang, Yongkai Chen, Haoran Lu, Wenxuan Zhong, Guo-Cheng Yuan, and Ping Ma. "Orthogonal multimodality integration and clustering in single-cell data." BMC Bioinformatics 25, no. 1 (2024). http://dx.doi.org/10.1186/s12859-024-05773-y.

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AbstractMultimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sourc

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43

Wagle, Manoj M., Siqu Long, Carissa Chen, Chunlei Liu, and Pengyi Yang. "Interpretable deep learning in single-cell omics." Bioinformatics, June 18, 2024. http://dx.doi.org/10.1093/bioinformatics/btae374.

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Abstract Motivation Single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field of machine learning, has instilled a significant interest in single-cell omics research due to its remarkable success in analysing heterogeneous high-dimensional single-cell omics data. Nevertheless, the inherent multi-layer nonlinear architecture of deep learning models often makes them ‘black boxes’ as the reasoning behind predictions is often unknown and not transparent to the user. This has st

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44

Wen, Lu, and Fuchou Tang. "Recent advances in single-cell sequencing technologies." Precision Clinical Medicine 5, no. 1 (2022). http://dx.doi.org/10.1093/pcmedi/pbac002.

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Abstract Single-cell omics sequencing was first achieved for the transcriptome in 2009, which was followed by fast development of technologies for profiling the genome, DNA methylome, 3D genome architecture, chromatin accessibility, histone modifications, etc., in an individual cell. In this review we mainly focus on the recent progress in four topics in the single-cell omics field: single-cell epigenome sequencing, single-cell genome sequencing for lineage tracing, spatially resolved single-cell transcriptomics and third-generation sequencing platform-based single-cell omics sequencing. We al

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45

Li, Yunfan, Dan Zhang, Mouxing Yang, et al. "scBridge embraces cell heterogeneity in single-cell RNA-seq and ATAC-seq data integration." Nature Communications 14, no. 1 (2023). http://dx.doi.org/10.1038/s41467-023-41795-5.

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AbstractSingle-cell multi-omics data integration aims to reduce the omics difference while keeping the cell type difference. However, it is daunting to model and distinguish the two differences due to cell heterogeneity. Namely, even cells of the same omics and type would have various features, making the two differences less significant. In this work, we reveal that instead of being an interference, cell heterogeneity could be exploited to improve data integration. Specifically, we observe that the omics difference varies in cells, and cells with smaller omics differences are easier to be int

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46

Kong, Siyuan, Rongrong Li, Yunhan Tian, et al. "Single-cell omics: A new direction for functional genetic research in human diseases and animal models." Frontiers in Genetics 13 (January 4, 2023). http://dx.doi.org/10.3389/fgene.2022.1100016.

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Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This rev

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47

Pan, Lu, Bufu Tang, Xuan Zhang, et al. "Comprehensive analysis of multi‐omics single‐cell data using the single‐cell analyst." iMeta, April 28, 2025. https://doi.org/10.1002/imt2.70038.

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AbstractThe rapid advancement of multi‐omics single‐cell technologies has significantly enhanced our ability to investigate complex biological systems at unprecedented resolution. However, many existing analysis tools are complex, requiring substantial coding expertize, which can be a barrier for computationally less competent researchers. To address this challenge, we present single‐cell analyst, a user‐friendly, web‐based platform to facilitate comprehensive multi‐omics analysis. Single‐cell analyst supports a wide range of data types, including six single‐cell omics: single‐cell RNA sequenc

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Wu, Xiangyu, Xin Yang, Yunhan Dai, et al. "Single-cell sequencing to multi-omics: technologies and applications." Biomarker Research 12, no. 1 (2024). http://dx.doi.org/10.1186/s40364-024-00643-4.

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AbstractCells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell fiel

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Ellis, Dorothy, Arkaprava Roy, and Susmita Datta. "Clustering single-cell multimodal omics data with jrSiCKLSNMF." Frontiers in Genetics 14 (June 9, 2023). http://dx.doi.org/10.3389/fgene.2023.1179439.

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Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise.Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-L

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Li, Yunjin, Lu Ma, Duojiao Wu, and Geng Chen. "Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine." Briefings in Bioinformatics, March 27, 2021. http://dx.doi.org/10.1093/bib/bbab024.

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Abstract Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summari

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