CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq Data
Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technique in molecular biology and genomics, revealing the cellular heterogeneity. However, scRNA-seq data often suffer from dropout events, meaning that certain genes exhibit very low or even zero expression levels due to technical limitation...
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MDPI AG
2025-01-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/1/31 |
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| author | Wanlin Juan Kwang Woo Ahn Yi-Guang Chen Chien-Wei Lin |
| author_facet | Wanlin Juan Kwang Woo Ahn Yi-Guang Chen Chien-Wei Lin |
| author_sort | Wanlin Juan |
| collection | DOAJ |
| description | Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technique in molecular biology and genomics, revealing the cellular heterogeneity. However, scRNA-seq data often suffer from dropout events, meaning that certain genes exhibit very low or even zero expression levels due to technical limitations. Existing imputation methods for dropout events lack comprehensive evaluations in downstream analyses and do not demonstrate robustness across various scenarios. In response to this challenge, we propose a consensus clustering-based imputation (CCI) method. CCI performs clustering on each subset of data sampling across genes and summarizes clustering outcomes to define cellular similarities. CCI leverages the information from similar cells and employs the similarities to impute gene expression levels. Our comprehensive evaluations demonstrate that CCI not only reconstructs the original data pattern, but also improves the performance of downstream analyses. CCI outperforms existing methods for data imputation under different scenarios, exhibiting accuracy, robustness, and generalization. |
| format | Article |
| id | doaj-art-c97fb958bbe54045a31118d6d93f6ea0 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-c97fb958bbe54045a31118d6d93f6ea02025-01-24T13:23:01ZengMDPI AGBioengineering2306-53542025-01-011213110.3390/bioengineering12010031CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq DataWanlin Juan0Kwang Woo Ahn1Yi-Guang Chen2Chien-Wei Lin3Division of Biostatistics, Data Science Institute, Medical College of Wisconsin (MCW), Milwaukee, WI 53226, USADivision of Biostatistics, Data Science Institute, Medical College of Wisconsin (MCW), Milwaukee, WI 53226, USADepartment of Pediatrics, Medical College of Wisconsin (MCW), Milwaukee, WI 53226, USADivision of Biostatistics, Data Science Institute, Medical College of Wisconsin (MCW), Milwaukee, WI 53226, USASingle-cell RNA sequencing (scRNA-seq) is a cutting-edge technique in molecular biology and genomics, revealing the cellular heterogeneity. However, scRNA-seq data often suffer from dropout events, meaning that certain genes exhibit very low or even zero expression levels due to technical limitations. Existing imputation methods for dropout events lack comprehensive evaluations in downstream analyses and do not demonstrate robustness across various scenarios. In response to this challenge, we propose a consensus clustering-based imputation (CCI) method. CCI performs clustering on each subset of data sampling across genes and summarizes clustering outcomes to define cellular similarities. CCI leverages the information from similar cells and employs the similarities to impute gene expression levels. Our comprehensive evaluations demonstrate that CCI not only reconstructs the original data pattern, but also improves the performance of downstream analyses. CCI outperforms existing methods for data imputation under different scenarios, exhibiting accuracy, robustness, and generalization.https://www.mdpi.com/2306-5354/12/1/31dropoutimputationconsensus clusteringscRNA-seq |
| spellingShingle | Wanlin Juan Kwang Woo Ahn Yi-Guang Chen Chien-Wei Lin CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq Data Bioengineering dropout imputation consensus clustering scRNA-seq |
| title | CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq Data |
| title_full | CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq Data |
| title_fullStr | CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq Data |
| title_full_unstemmed | CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq Data |
| title_short | CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq Data |
| title_sort | cci a consensus clustering based imputation method for addressing dropout events in scrna seq data |
| topic | dropout imputation consensus clustering scRNA-seq |
| url | https://www.mdpi.com/2306-5354/12/1/31 |
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