Full metadata record
DC Field | Value | Language |
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dc.citation.number | 1 | - |
dc.citation.startPage | 1570 | - |
dc.citation.title | NATURE COMMUNICATIONS | - |
dc.citation.volume | 14 | - |
dc.contributor.author | Nguyen, Hai C. T. | - |
dc.contributor.author | Baik, Bukyung | - |
dc.contributor.author | Yoon, Sora | - |
dc.contributor.author | Park, Taesung | - |
dc.contributor.author | Nam, Dougu | - |
dc.date.accessioned | 2023-12-21T12:45:01Z | - |
dc.date.available | 2023-12-21T12:45:01Z | - |
dc.date.created | 2023-08-09 | - |
dc.date.issued | 2023-03 | - |
dc.description.abstract | Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. Here the authors benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches and suggest several high-performance methods under different conditions based on simulation and real data analyses. Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. However, strategies to integrate differential expression analysis of single-cell data remain underinvestigated. Here, we benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches. We show that batch effects, sequencing depth and data sparsity substantially impact their performances. Notably, we find that the use of batch-corrected data rarely improves the analysis for sparse data, whereas batch covariate modeling improves the analysis for substantial batch effects. We show that for low depth data, single-cell techniques based on zero-inflation model deteriorate the performance, whereas the analysis of uncorrected data using limmatrend, Wilcoxon test and fixed effects model performs well. We suggest several high-performance methods under different conditions based on various simulation and real data analyses. Additionally, we demonstrate that differential expression analysis for a specific cell type outperforms that of large-scale bulk sample data in prioritizing disease-related genes. | - |
dc.identifier.bibliographicCitation | NATURE COMMUNICATIONS, v.14, no.1, pp.1570 | - |
dc.identifier.doi | 10.1038/s41467-023-37126-3 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.scopusid | 2-s2.0-85150670855 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/65212 | - |
dc.identifier.wosid | 000984168400020 | - |
dc.language | 영어 | - |
dc.publisher | NATURE PORTFOLIO | - |
dc.title | Benchmarking integration of single-cell differential expression | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | RNA-SEQ EXPERIMENTS | - |
dc.subject.keywordPlus | QUALITY-CONTROL | - |
dc.subject.keywordPlus | INFECTION | - |
dc.subject.keywordPlus | PACKAGE | - |
dc.subject.keywordPlus | CANCER | - |
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