Comprehensive analysis of multi-omics single-cell data using the single-cell analyst

Lu Pan , Bufu Tang , Xuan Zhang , Paolo Parini , Roman Tremmel , Joseph Loscalzo , Volker M. Lauschke , Bradley A. Maron , Paola Paci , Ingemar Ernberg , Nguan Soon Tan , Ákos Végvári , Zehuan Liao , Sundararaman Rengarajan , Roman Zubarev , Yuxuan Fan , Xu Zheng , Xinyue Jian , Ren Sheng , Zhenning Wang , Xuexin Li

iMeta ›› 2025, Vol. 4 ›› Issue (3) : e70038

PDF
iMeta ›› 2025, Vol. 4 ›› Issue (3) :e70038 DOI: 10.1002/imt2.70038
RESEARCH ARTICLE
Comprehensive analysis of multi-omics single-cell data using the single-cell analyst
Author information +
History +
PDF

Abstract

The 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 sequencing (scRNA-sequencing), single-cell assay for transposase accessible chromatin sequencing (scATAC-seq sequencing), single-cell immune profiling (scImmune profiling), single-cell copy number variation, cytometry by time-of-flight, and flow cytometry and spatial transcriptomics, and enables researchers to perform integrated analyses without requiring programming skills. The platform offers both online and offline modes, providing flexibility for various use cases. It automates critical analysis steps, such as quality control, data processing, and phenotype-specific analyses, while also offering interactive, publication-ready visualizations. With over 20 interactive tools for intermediate analysis, single cell analyst simplifies workflows and significantly reduces the learning curve typically associated with similar platforms. This robust tool accommodates datasets of varying sizes, completing analyses within minutes to hours depending on the data volume, and ensures efficient use of computational resources. By democratizing the complex process of multi-omics analysis, single-cell analyst serves as an accessible, all-encompassing solution for researchers of diverse technical backgrounds. The platform is freely accessible at www.singlecellanalyst.org.

Keywords

multi-omics / single-cell sequencing / web server

Cite this article

Download citation ▾
Lu Pan, Bufu Tang, Xuan Zhang, Paolo Parini, Roman Tremmel, Joseph Loscalzo, Volker M. Lauschke, Bradley A. Maron, Paola Paci, Ingemar Ernberg, Nguan Soon Tan, Ákos Végvári, Zehuan Liao, Sundararaman Rengarajan, Roman Zubarev, Yuxuan Fan, Xu Zheng, Xinyue Jian, Ren Sheng, Zhenning Wang, Xuexin Li. Comprehensive analysis of multi-omics single-cell data using the single-cell analyst. iMeta, 2025, 4(3): e70038 DOI:10.1002/imt2.70038

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Lee, Jeongwoo, Do Young Hyeon, and Daehee Hwang. 2020. “Single-Cell Multiomics: Technologies and Data Analysis Methods.” Experimental & Molecular Medicine 52: 1428-1442. https://doi.org/10.1038/s12276-020-0420-2

[2]

Zappia, Luke, and Fabian J. Theis. 2021. “Over 1000 Tools Reveal Trends in the Single-Cell RNA-seq Analysis Landscape.” Genome Biology 22: 301. https://doi.org/10.1186/s13059-021-02519-4

[3]

Moreno, Pablo, Ni Huang, Jonathan R. Manning, Suhaib Mohammed, Andrey Solovyev, Krzysztof Polanski, Wendi Bacon, et al. 2021. “User-Friendly, Scalable Tools and Workflows for Single-Cell RNA-seq Analysis.” Nature Methods 18: 327-328. https://doi.org/10.1038/s41592-021-01102-w

[4]

Kolodziejczyk, Aleksandra A., Jong Kyoung Kim, Valentine Svensson, John C. Marioni, and Sarah A. Teichmann. 2015. “The Technology and Biology of Single-Cell RNA Sequencing.” Molecular Cell 58: 610-620. https://doi.org/10.1016/j.molcel.2015.04.005

[5]

Sethi, Raman, Kok Siong Ang, Mengwei Li, Yahui Long, Jingjing Ling, and Jinmiao Chen. 2024. “ezSingleCell: An Integrated One-Stop Single-Cell and Spatial Omics Analysis Platform for Bench Scientists.” Nature Communications 15: 5600. https://doi.org/10.1038/s41467-024-48188-2

[6]

Hasanaj, Euxhen, Jingtao Wang, Arjun Sarathi, Jun Ding, and Ziv Bar-Joseph. 2022. “Interactive Single-Cell Data Analysis Using Cellar.” Nature Communications 13: 1998. https://doi.org/10.1038/s41467-022-29744-0

[7]

Jiang, Andrew, Klaus Lehnert, Linya You, and Russell G. Snell. 2022. “ICARUS, an Interactive Web Server for Single Cell RNA-seq Analysis.” Nucleic Acids Research 50: W427-W433. https://doi.org/10.1093/nar/gkac322

[8]

Afgan, Enis, Dannon Baker, Bérénice Batut, Marius van den Beek, Dave Bouvier, Martin Čech, John Chilton, et al. 2018. “The Galaxy Platform for Accessible, Reproducible and Collaborative Biomedical Analyses: 2018 Update.” Nucleic Acids Research 46: W537-W544. https://doi.org/10.1093/nar/gky379

[9]

Schwartzman, Omer, and Amos Tanay. 2015. “Single-Cell Epigenomics: Techniques and Emerging Applications.” Nature Reviews Genetics 16: 716-726. https://doi.org/10.1038/nrg3980

[10]

Gomes, Tomás, Sarah A. Teichmann, and Carlos Talavera-López. 2019. “Immunology Driven by Large-Scale Single-Cell Sequencing.” Trends in Immunology 40: 1011-1021. https://doi.org/10.1016/j.it.2019.09.004

[11]

Garvin, Tyler, Robert Aboukhalil, Jude Kendall, Timour Baslan, Gurinder S. Atwal, James Hicks, Michael Wigler, and Michael C. Schatz. 2015. “Interactive Analysis and Assessment of Single-Cell Copy-Number Variations.” Nature Methods 12: 1058-1060. https://doi.org/10.1038/nmeth.3578

[12]

Cheung, Regina K., and Paul J. Utz. 2011. “CyTOF—The Next Generation of Cell Detection.” Nature Reviews Rheumatology 7: 502-503. https://doi.org/10.1038/nrrheum.2011.110

[13]

Spitzer, Matthew H., and Garry P. Nolan. 2016. “Mass Cytometry: Single Cells, Many Features.” Cell 165: 780-791. https://doi.org/10.1016/j.cell.2016.04.019

[14]

Tian, Yuan, Lindsay N. Carpp, Helen E. R. Miller, Michael Zager, and Evan W. Newell. 2022. “Single-Cell Immunology of SARS-CoV-2 Infection.” Nature Biotechnology 40: 30-41. https://doi.org/10.1038/s41587-021-01131-y

[15]

McKinnon, Katherine M. 2018. “Flow Cytometry: An Overview.” Current Protocols in Immunology 120: 5.1.1-5.1.11. https://doi.org/10.1002/cpim.40

[16]

Rao, Anjali, Dalia Barkley, Gustavo S. França, and Itai Yanai. 2021. “Exploring Tissue Architecture Using Spatial Transcriptomics.” Nature 596: 211-220. https://doi.org/10.1038/s41586-021-03634-9

[17]

Pan, Lu, Paolo Parini, Roman Tremmel, Joseph Loscalzo, Volker M. Lauschke, Bradley A. Maron, Paola Paci, et al. 2024. “Single Cell Atlas: A Single-Cell Multi-Omics Human Cell Encyclopedia.” Genome Biology 25: 104. https://doi.org/10.1186/s13059-024-03246-2

[18]

Pan, Lu, Tian Mou, Yue Huang, Weifeng Hong, Min Yu, and Xuexin Li. 2023. “Ursa: A Comprehensive Multiomics Toolbox for High-Throughput Single-Cell Analysis.” Molecular Biology and Evolution 40: msad267. https://doi.org/10.1093/molbev/msad267

[19]

Jombart, Thibaut, Sébastien Devillard, and François Balloux. 2010. “Discriminant Analysis of Principal Components: A New Method for the Analysis of Genetically Structured Populations.” BMC Genetics 11: 94. https://doi.org/10.1186/1471-2156-11-94

[20]

Gardeux, Vincent, Fabrice P. A. David, Adrian Shajkofci, Petra C. Schwalie, and Bart Deplancke. 2017. “ASAP: A Web-Based Platform for the Analysis and Interactive Visualization of Single-Cell RNA-seq Data.” Bioinformatics 33: 3123-3125. https://doi.org/10.1093/bioinformatics/btx337

[21]

Prieto, Carlos, David Barrios, and Angela Villaverde. 2022. “SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud.” Frontiers in Bioinformatics 2: 793309. https://doi.org/10.3389/fbinf.2022.793309

[22]

Pereira, W. J., F. M. Almeida, D. Conde, K. M. Balmant, P. M. Triozzi, H. W. Schmidt, C., Dervinis, G. J. Pappas, and M. Kirst. 2021. “Asc-Seurat: Analytical Single-Cell Seurat-Based Web Application.” BMC Bioinformatics 22: 556. https://doi.org/10.1186/s12859-021-04472-2

[23]

Franzén, Oscar, and Johan L. M. Björkegren. 2020. “Alona: A Web Server for Single-Cell RNA-seq Analysis.” Bioinformatics 36: 3910-3912. https://doi.org/10.1093/bioinformatics/btaa269

[24]

Wang, Yichen, Irzam Sarfraz, Rui Hong, Yusuke Koga, Vidya Akavoor, Xinyun Cao, Salam Alabdullatif, et al. 2023. “Interactive Analysis of Single-Cell Data Using Flexible Workflows With SCTK2.” Patterns.” 4: 100814. https://doi.org/10.1101/2022.07.13.499900

[25]

Yousif, Ayman, Nizar Drou, Jillian Rowe, Mohammed Khalfan, and Kristin C. Gunsalus. 2020. “NASQAR: A Web-Based Platform for High-Throughput Sequencing Data Analysis and Visualization.” BMC Bioinformatics 21: 267. https://doi.org/10.1186/s12859-020-03577-4

[26]

Taverna, Federico, Jermaine Goveia, Tobias K. Karakach, Shawez Khan, Katerina Rohlenova, Lucas Treps, Abhishek Subramanian, et al. 2020. “BIOMEX: An Interactive Workflow for (Single Cell) Omics Data Interpretation and Visualization.” Nucleic Acids Research 48: W385-W394. https://doi.org/10.1093/nar/gkaa332

[27]

Hao, Yuhan, Stephanie Hao, Erica Andersen-Nissen, William M. Mauck, Shiwei Zheng, Andrew Butler, Maddie J. Lee, et al. 2021. “Integrated Analysis of Multimodal Single-Cell Data.” Cell 184: 3573-3587.e29. https://doi.org/10.1016/j.cell.2021.04.048

[28]

Trapnell, Cole, Davide, Cacchiarelli, Jonna Grimsby, Prapti Pokharel, Shuqiang Li, Michael Morse, Niall J. Lennon, et al. 2014. “The Dynamics and Regulators of Cell Fate Decisions Are Revealed by Pseudotemporal Ordering of Single Cells.” Nature Biotechnology 32: 381-386. https://doi.org/10.1038/nbt.2859

[29]

Qiu, Xiaojie, Qi Mao, Ying Tang, Li Wang, Raghav Chawla, Hannah A. Pliner, and Cole Trapnell. 2017. “Reversed Graph Embedding Resolves Complex Single-Cell Trajectories.” Nature Methods 14: 979-982. https://doi.org/10.1038/nmeth.4402

[30]

Cao, Junyue, Malte Spielmann, Xiaojie Qiu, Xingfan Huang, Daniel M. Ibrahim, Andrew J. Hill, Fan Zhang, et al. 2019. “The Single-Cell Transcriptional Landscape of Mammalian Organogenesis.” Nature 566: 496-502. https://doi.org/10.1038/s41586-019-0969-x

[31]

Stuart, Tim, Avi Srivastava, Shaista Madad, Caleb A. Lareau, and Rahul Satija. 2021. “Single-Cell Chromatin State Analysis With Signac.” Nature Methods 18: 1333-1341. https://doi.org/10.1038/s41592-021-01282-5

[32]

Borcherding, Nicholas, and Nicholas L. Bormann. 2020. “Screpertoire: An R-Based Toolkit for Single-Cell Immune Receptor Analysis.” F1000Research 9: 47. https://doi.org/10.12688/f1000research.22139.1

[33]

Aran, Dvir, Agnieszka P. Looney, Leqian Liu, Esther Wu, Valerie Fong, Austin Hsu, Suzanna Chak, et al. 2019. “Reference-Based Analysis of Lung Single-Cell Sequencing Reveals a Transitional Profibrotic Macrophage.” Nature Immunology 20: 163-172. https://doi.org/10.1038/s41590-018-0276-y

[34]

Piñero, Janet, Juan Manuel Ramírez-Anguita, Josep Saüch-Pitarch, Francesco Ronzano, Emilio Centeno, Ferran Sanz, and Laura I. Furlong. 2020. “The DisGeNET Knowledge Platform for Disease Genomics: 2019 Update.” Nucleic Acids Research 48: D845-D855. https://doi.org/10.1093/nar/gkz1021

[35]

Yu, Guangchuang, Li-Gen Wang, Guang-Rong Yan, and Qing-Yu He. 2015. “DOSE: An R/Bioconductor Package for Disease Ontology Semantic and Enrichment Analysis.” Bioinformatics 31: 608-609. https://doi.org/10.1093/bioinformatics/btu684

[36]

Wu, Tianzhi, Erqiang Hu, Shuangbin Xu, Meijun Chen, Pingfan Guo, Zehan Dai, Tingze Feng, et al. 2021. “Clusterprofiler 4.0: A Universal Enrichment Tool for Interpreting Omics Data.” Innovation 2: 100141. https://doi.org/10.1016/j.xinn.2021.100141

[37]

Yu, Guangchuang, and Qing-yu He. 2016. “ReactomePA: An R/Bioconductor Package for Reactome Pathway Analysis and Visualization.” Molecular BioSystems 12: 477-479. https://doi.org/10.1039/c5mb00663e

[38]

Castro-Mondragon, Jaime A., Rafael Riudavets-Puig, Ieva Rauluseviciute, Roza Berhanu Lemma, Laura Turchi, Romain Blanc-Mathieu, Jeremy Lucas, et al. 2022. “JASPAR 2022: The 9th Release of the Open-Access Database of Transcription Factor Binding Profiles.” Nucleic Acids Research 50: D165-D173. https://doi.org/10.1093/nar/gkab1113

[39]

Nowicka, Malgorzata, Carsten Krieg, Lukas M. Weber, Felix J. Hartmann, Silvia Guglietta, Burkhard Becher, Mitchell P. Levesque, and Mark D. Robinson. 2017. “CyTOF Workflow: Differential Discovery in High-Throughput High-Dimensional Cytometry Datasets.” F1000Research 6: 748. https://doi.org/10.12688/f1000research.11622.1

[40]

Van Gassen, Sofie, Britt Callebaut, Mary J., Van Helden, Bart N., Lambrecht, Piet Demeester, Tom Dhaene, Yvan Saeys, et al. 2015. “FlowSOM: Using Self-Organizing Maps for Visualization and Interpretation of Cytometry Data.” Cytometry Part A 87: 636-645. https://doi.org/10.1002/cyto.a.22625

RIGHTS & PERMISSIONS

2025 The Author(s). iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.

PDF

0

Accesses

0

Citation

Detail

Sections
Recommended

/