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Zhou M, Zhang H, Bai Z, Mann-Krzisnik D, Wang F, Li Y. Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique. STAR Protoc 2024; 5:103066. [PMID: 38748882 PMCID: PMC11109308 DOI: 10.1016/j.xpro.2024.103066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/21/2023] [Accepted: 04/24/2024] [Indexed: 05/25/2024] Open
Abstract
The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells. Here, we present a protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique called moETM. We describe steps for data preprocessing, multi-omics integration, inclusion of prior pathway knowledge, and cross-omics imputation. As a demonstration, we used the single-cell multi-omics data collected from bone marrow mononuclear cells (GSE194122) as in our original study. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.
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Affiliation(s)
- Manqi Zhou
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA; Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY 10021, USA
| | - Hao Zhang
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10021, USA
| | - Zilong Bai
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY 10021, USA; Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10021, USA
| | | | - Fei Wang
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY 10021, USA; Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10021, USA
| | - Yue Li
- Quantitative Life Science, McGill University, Montréal, QC H3A 0G4, Canada; School of Computer Science, McGill University, Montréal, QC H3A 0G4, Canada; Mila - Quebec AI Institute, Montréal, QC H2S 3H1, Canada.
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Salido-Guadarrama I, Romero-Cordoba SL, Rueda-Zarazua B. Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer. Int J Mol Sci 2023; 24:16600. [PMID: 38068923 PMCID: PMC10706612 DOI: 10.3390/ijms242316600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023] Open
Abstract
In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.
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Affiliation(s)
- Ivan Salido-Guadarrama
- Departamento de Bioinformatìca y Análisis Estadísticos, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico
| | - Sandra L. Romero-Cordoba
- Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
- Biochemistry Department, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City 14080, Mexico
| | - Bertha Rueda-Zarazua
- Posgrado en Ciencias Biológicas, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
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