دورية أكاديمية
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A benchmark of batch-effect correction methods for single-cell RNA sequencing data.

التفاصيل البيبلوغرافية
العنوان: A benchmark of batch-effect correction methods for single-cell RNA sequencing data.
المؤلفون: Tran HTN; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore., Ang KS; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore., Chevrier M; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore., Zhang X; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore., Lee NYS; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore., Goh M; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore., Chen J; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos Building, Level 3, Singapore, 138648, Singapore. chen_jinmiao@immunol.a-star.edu.sg.
المصدر: Genome biology [Genome Biol] 2020 Jan 16; Vol. 21 (1), pp. 12. Date of Electronic Publication: 2020 Jan 16.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 100960660 Publication Model: Electronic Cited Medium: Internet ISSN: 1474-760X (Electronic) Linking ISSN: 14747596 NLM ISO Abbreviation: Genome Biol Subsets: MEDLINE
أسماء مطبوعة: Publication: London, UK : BioMed Central Ltd
Original Publication: London : Genome Biology Ltd., c2000-
مواضيع طبية MeSH: RNA-Seq/*methods , Single-Cell Analysis/*methods, Algorithms ; Animals ; Benchmarking ; Big Data ; Humans ; Mice
مستخلص: Background: Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data integration. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most suitable method for batch-effect removal.
Results: We compare 14 methods in terms of computational runtime, the ability to handle large datasets, and batch-effect correction efficacy while preserving cell type purity. Five scenarios are designed for the study: identical cell types with different technologies, non-identical cell types, multiple batches, big data, and simulated data. Performance is evaluated using four benchmarking metrics including kBET, LISI, ASW, and ARI. We also investigate the use of batch-corrected data to study differential gene expression.
Conclusion: Based on our results, Harmony, LIGER, and Seurat 3 are the recommended methods for batch integration. Due to its significantly shorter runtime, Harmony is recommended as the first method to try, with the other methods as viable alternatives.
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فهرسة مساهمة: Keywords: Batch correction; Batch effect; Differential gene expression; Integration; Single-cell RNA-seq
تواريخ الأحداث: Date Created: 20200118 Date Completed: 20200609 Latest Revision: 20240327
رمز التحديث: 20240327
مُعرف محوري في PubMed: PMC6964114
DOI: 10.1186/s13059-019-1850-9
PMID: 31948481
قاعدة البيانات: MEDLINE
الوصف
تدمد:1474-760X
DOI:10.1186/s13059-019-1850-9