Journal
BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 5, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab105
Keywords
single-cell RNA sequencing; clustering; trajectory inference; cell type annotation; integrating datasets
Funding
- National Key R&D Program of China [2018YFC0910405]
- National Natural Science Foundation of China [61922020, 61771331, 91935302]
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This review summarizes the most widely used methods in single-cell RNA sequencing data analysis, covering clustering, trajectory inference, cell-type annotation, and dataset integration. Advantages and limitations of these methods are comprehensively discussed, providing suggestions for selecting appropriate methods in different situations.
Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.
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