Literature DB >> 32818441

Integrative Methods and Practical Challenges for Single-Cell Multi-omics.

Anjun Ma1, Adam McDermaid2, Jennifer Xu3, Yuzhou Chang1, Qin Ma4.   

Abstract

Fast-developing single-cell multimodal omics (scMulti-omics) technologies enable the measurement of multiple modalities, such as DNA methylation, chromatin accessibility, RNA expression, protein abundance, gene perturbation, and spatial information, from the same cell. scMulti-omics can comprehensively explore and identify cell characteristics, while also presenting challenges to the development of computational methods and tools for integrative analyses. Here, we review these integrative methods and summarize the existing tools for studying a variety of scMulti-omics data. The various functionalities and practical challenges in using the available tools in the public domain are explored through several case studies. Finally, we identify remaining challenges and future trends in scMulti-omics modeling and analyses.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  analysis tools; integrative methods; single-cell multi-modality; single-cell sequencing technology

Mesh:

Year:  2020        PMID: 32818441      PMCID: PMC7442857          DOI: 10.1016/j.tibtech.2020.02.013

Source DB:  PubMed          Journal:  Trends Biotechnol        ISSN: 0167-7799            Impact factor:   19.536


  106 in total

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Authors:  Charles Gawad; Winston Koh; Stephen R Quake
Journal:  Nat Rev Genet       Date:  2016-01-25       Impact factor: 53.242

Review 2.  The origin and evolution of cell types.

Authors:  Detlev Arendt; Jacob M Musser; Clare V H Baker; Aviv Bergman; Connie Cepko; Douglas H Erwin; Mihaela Pavlicev; Gerhard Schlosser; Stefanie Widder; Manfred D Laubichler; Günter P Wagner
Journal:  Nat Rev Genet       Date:  2016-11-07       Impact factor: 53.242

3.  Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics.

Authors:  Nikolaos Perakakis; Alireza Yazdani; George E Karniadakis; Christos Mantzoros
Journal:  Metabolism       Date:  2018-08-08       Impact factor: 8.694

4.  Single-cell multi-omics sequencing of human early embryos.

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Journal:  Nat Cell Biol       Date:  2018-06-18       Impact factor: 28.824

5.  G&T-seq: parallel sequencing of single-cell genomes and transcriptomes.

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Journal:  Nat Methods       Date:  2015-04-27       Impact factor: 28.547

6.  Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations.

Authors:  Zhana Duren; Xi Chen; Mahdi Zamanighomi; Wanwen Zeng; Ansuman T Satpathy; Howard Y Chang; Yong Wang; Wing Hung Wong
Journal:  Proc Natl Acad Sci U S A       Date:  2018-07-09       Impact factor: 11.205

7.  Unsupervised embedding of single-cell Hi-C data.

Authors:  Jie Liu; Dejun Lin; Galip Gürkan Yardimci; William Stafford Noble
Journal:  Bioinformatics       Date:  2018-07-01       Impact factor: 6.937

8.  Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma.

Authors:  Li Zhang; Chenkai Lv; Yaqiong Jin; Ganqi Cheng; Yibao Fu; Dongsheng Yuan; Yiran Tao; Yongli Guo; Xin Ni; Tieliu Shi
Journal:  Front Genet       Date:  2018-10-18       Impact factor: 4.599

9.  Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity.

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Journal:  Nat Commun       Date:  2019-01-28       Impact factor: 14.919

10.  More than one antibody of individual B cells revealed by single-cell immune profiling.

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Journal:  Cell Discov       Date:  2019-12-10       Impact factor: 10.849

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  43 in total

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Review 3.  Machine learning: its challenges and opportunities in plant system biology.

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Review 4.  Deciphering functional tumor states at single-cell resolution.

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Review 5.  Multiplex protein analysis for the study of glaucoma.

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Journal:  Expert Rev Proteomics       Date:  2021-10-29       Impact factor: 3.940

Review 6.  Computational principles and challenges in single-cell data integration.

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Journal:  Nat Biotechnol       Date:  2021-05-03       Impact factor: 54.908

Review 7.  New horizons in the stormy sea of multimodal single-cell data integration.

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Journal:  Mol Cell       Date:  2022-01-20       Impact factor: 17.970

Review 8.  A Sight on Single-Cell Transcriptomics in Plants Through the Prism of Cell-Based Computational Modeling Approaches: Benefits and Challenges for Data Analysis.

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Review 9.  Microfluidics in Single-Cell Virology: Technologies and Applications.

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Review 10.  Applications of Single-Cell Omics in Tumor Immunology.

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Journal:  Front Immunol       Date:  2021-06-09       Impact factor: 7.561

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