Benchmarking single-cell cross-omics imputation methods for surface protein expression
Abstract Background Recent advances in single-cell multimodal omics sequencing have facilitated the simultaneous profiling of transcriptomes and surface proteomes within individual cells, offering insights into cellular functions and heterogeneity. However, the high costs and technical complexity of...
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| Format: | Article |
| Language: | English |
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BMC
2025-03-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-025-03514-9 |
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| author | Chen-Yang Li Yong-Jia Hong Bo Li Xiao-Fei Zhang |
| author_facet | Chen-Yang Li Yong-Jia Hong Bo Li Xiao-Fei Zhang |
| author_sort | Chen-Yang Li |
| collection | DOAJ |
| description | Abstract Background Recent advances in single-cell multimodal omics sequencing have facilitated the simultaneous profiling of transcriptomes and surface proteomes within individual cells, offering insights into cellular functions and heterogeneity. However, the high costs and technical complexity of protocols like CITE-seq and REAP-seq constrain large-scale dataset generation. To overcome this limitation, surface protein data imputation methods have emerged to predict protein abundances from scRNA-seq data. Results We present a comprehensive benchmark of twelve state-of-the-art imputation methods across eleven datasets and six scenarios. Our analysis evaluates the methods’ accuracy, sensitivity to training data size, robustness across experiments, and usability in terms of running time, memory usage, popularity, and user-friendliness. With benchmark experiments in diverse scenarios and a comprehensive evaluation framework of the results, our study offers valuable insights into the performance and applicability of surface protein data imputation methods in single-cell omics research. Conclusions Based on our results, Seurat v4 (PCA) and Seurat v3 (PCA) demonstrate exceptional performance, offering promising avenues for further research in single-cell omics. |
| format | Article |
| id | doaj-art-445971c9ac834e38a9d30d76f0ccd0fc |
| institution | DOAJ |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-445971c9ac834e38a9d30d76f0ccd0fc2025-08-20T02:59:57ZengBMCGenome Biology1474-760X2025-03-0126113010.1186/s13059-025-03514-9Benchmarking single-cell cross-omics imputation methods for surface protein expressionChen-Yang Li0Yong-Jia Hong1Bo Li2Xiao-Fei Zhang3School of Mathematics and Statistics, and Hubei Key Lab–Math. Sci., Central China Normal UniversitySchool of Mathematics and Statistics, and Hubei Key Lab–Math. Sci., Central China Normal UniversitySchool of Mathematics and Statistics, and Hubei Key Lab–Math. Sci., Central China Normal UniversitySchool of Mathematics and Statistics, and Hubei Key Lab–Math. Sci., Central China Normal UniversityAbstract Background Recent advances in single-cell multimodal omics sequencing have facilitated the simultaneous profiling of transcriptomes and surface proteomes within individual cells, offering insights into cellular functions and heterogeneity. However, the high costs and technical complexity of protocols like CITE-seq and REAP-seq constrain large-scale dataset generation. To overcome this limitation, surface protein data imputation methods have emerged to predict protein abundances from scRNA-seq data. Results We present a comprehensive benchmark of twelve state-of-the-art imputation methods across eleven datasets and six scenarios. Our analysis evaluates the methods’ accuracy, sensitivity to training data size, robustness across experiments, and usability in terms of running time, memory usage, popularity, and user-friendliness. With benchmark experiments in diverse scenarios and a comprehensive evaluation framework of the results, our study offers valuable insights into the performance and applicability of surface protein data imputation methods in single-cell omics research. Conclusions Based on our results, Seurat v4 (PCA) and Seurat v3 (PCA) demonstrate exceptional performance, offering promising avenues for further research in single-cell omics.https://doi.org/10.1186/s13059-025-03514-9Single-cell multimodal omicsSingle-cell RNA-seqSurface protein expressionCross-omics imputationBenchmark |
| spellingShingle | Chen-Yang Li Yong-Jia Hong Bo Li Xiao-Fei Zhang Benchmarking single-cell cross-omics imputation methods for surface protein expression Genome Biology Single-cell multimodal omics Single-cell RNA-seq Surface protein expression Cross-omics imputation Benchmark |
| title | Benchmarking single-cell cross-omics imputation methods for surface protein expression |
| title_full | Benchmarking single-cell cross-omics imputation methods for surface protein expression |
| title_fullStr | Benchmarking single-cell cross-omics imputation methods for surface protein expression |
| title_full_unstemmed | Benchmarking single-cell cross-omics imputation methods for surface protein expression |
| title_short | Benchmarking single-cell cross-omics imputation methods for surface protein expression |
| title_sort | benchmarking single cell cross omics imputation methods for surface protein expression |
| topic | Single-cell multimodal omics Single-cell RNA-seq Surface protein expression Cross-omics imputation Benchmark |
| url | https://doi.org/10.1186/s13059-025-03514-9 |
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