Journal article
Computational Methods for Single-cell DNA Methylome Analysis
Genomics, proteomics & bioinformatics, v 21(1), pp 48-66
01 Feb 2023
PMID: 35718270
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.
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Details
- Title
- Computational Methods for Single-cell DNA Methylome Analysis
- Creators
- Waleed Iqbal - Children's Hospital of PhiladelphiaWanding Zhou (Corresponding Author) - Children's Hospital of Philadelphia
- Publication Details
- Genomics, proteomics & bioinformatics, v 21(1), pp 48-66
- Publisher
- Oxford University Press
- Number of pages
- 19
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Pharmacology and Physiology
- Web of Science ID
- WOS:001052566800001
- Scopus ID
- 2-s2.0-85144861992
- Other Identifier
- 991022083952004721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Genetics & Heredity