scRAA : the development of a robust and automatic annotation procedure for single-cell RNA sequencing data
A critical task in single-cell RNA sequencing (scRNA-Seq) data analysis is to identify cell types from heterogeneous tissues. While the majority of classification methods demonstrated high performance in scRNA-Seq annotation problems, a robust and accurate solution is desired to generate reliable outcomes for downstream analyses, for instance, marker genes identification, differentially expressed genes, and pathway analysis. It is hard to establish a universally good metric. Thus, a universally good classification method for all kinds of scenarios does not exist. In addition, reference and query data in cell classification are usually from different experimental batches, and failure to consider batch effects may result in misleading conclusions. To overcome this bottleneck, we propose a robust ensemble approach to classify cells and utilize a batch correction method between reference and query data. We simulated four scenarios that comprise simple to complex batch effect and account for varying cell-type proportions. We further tested our approach on both lung and pancreas data. We found improved prediction accuracy and robust performance across simulation scenarios and real data. The incorporation of batch effect correction between reference and query, and the ensemble approach improve cell-type prediction accuracy while maintaining robustness. We demonstrated these through simulated and real scRNA-Seq data.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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| Enthalten in: |
Journal of biopharmaceutical statistics - (2023) vom: 10. Mai, Seite 1-14 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yan, Dongyan [VerfasserIn] |
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| Links: |
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| Themen: |
Batch effect correction |
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| Anmerkungen: |
Date Revised 10.05.2023 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1080/10543406.2023.2208671 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM35665205X |
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| 520 | |a A critical task in single-cell RNA sequencing (scRNA-Seq) data analysis is to identify cell types from heterogeneous tissues. While the majority of classification methods demonstrated high performance in scRNA-Seq annotation problems, a robust and accurate solution is desired to generate reliable outcomes for downstream analyses, for instance, marker genes identification, differentially expressed genes, and pathway analysis. It is hard to establish a universally good metric. Thus, a universally good classification method for all kinds of scenarios does not exist. In addition, reference and query data in cell classification are usually from different experimental batches, and failure to consider batch effects may result in misleading conclusions. To overcome this bottleneck, we propose a robust ensemble approach to classify cells and utilize a batch correction method between reference and query data. We simulated four scenarios that comprise simple to complex batch effect and account for varying cell-type proportions. We further tested our approach on both lung and pancreas data. We found improved prediction accuracy and robust performance across simulation scenarios and real data. The incorporation of batch effect correction between reference and query, and the ensemble approach improve cell-type prediction accuracy while maintaining robustness. We demonstrated these through simulated and real scRNA-Seq data | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a cell-type classification | |
| 650 | 4 | |a ensembled method | |
| 700 | 1 | |a Sun, Zhe |e verfasserin |4 aut | |
| 700 | 1 | |a Fang, Jiyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Cao, Shanshan |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Wenjie |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Xinyue |e verfasserin |4 aut | |
| 700 | 1 | |a Badirli, Sarkhan |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Haoda |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yushi |e verfasserin |4 aut | |
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