Imputation Methods for scRNA Sequencing Data
More and more researchers use single-cell RNA sequencing (scRNA-seq) technology to characterize the transcriptional map at the single-cell level. They use it to study the heterogeneity of complex tissues, transcriptome dynamics, and the diversity of unknown organisms. However, there are generally lo...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/20/10684 |
| _version_ | 1797475466584522752 |
|---|---|
| author | Mengyuan Wang Jiatao Gan Changfeng Han Yanbing Guo Kaihao Chen Ya-zhou Shi Ben-gong Zhang |
| author_facet | Mengyuan Wang Jiatao Gan Changfeng Han Yanbing Guo Kaihao Chen Ya-zhou Shi Ben-gong Zhang |
| author_sort | Mengyuan Wang |
| collection | DOAJ |
| description | More and more researchers use single-cell RNA sequencing (scRNA-seq) technology to characterize the transcriptional map at the single-cell level. They use it to study the heterogeneity of complex tissues, transcriptome dynamics, and the diversity of unknown organisms. However, there are generally lots of technical and biological noises in the scRNA-seq data since the randomness of gene expression patterns. These data are often characterized by high-dimension, sparsity, large number of “dropout” values, and affected by batch effects. A large number of “dropout” values in scRNA-seq data seriously conceal the important relationship between genes and hinder the downstream analysis. Therefore, the imputation of dropout values of scRNA-seq data is particularly important. We classify, analyze and compare the current advanced scRNA-seq data imputation methods from different angles. Through the comparison and analysis of the principle, advantages and disadvantages of the algorithm, it can provide suggestions for the selection of imputation methods for specific problems and diverse data, and have basic research significance for the downstream function analysis of data. |
| first_indexed | 2024-03-09T20:44:32Z |
| format | Article |
| id | doaj.art-3ec42c679fb4453e92f578c1accad06a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:44:32Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3ec42c679fb4453e92f578c1accad06a2023-11-23T22:48:44ZengMDPI AGApplied Sciences2076-34172022-10-0112201068410.3390/app122010684Imputation Methods for scRNA Sequencing DataMengyuan Wang0Jiatao Gan1Changfeng Han2Yanbing Guo3Kaihao Chen4Ya-zhou Shi5Ben-gong Zhang6Center of Applied Mathematics & Interdisciplinary Sciences, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430200, ChinaSchool of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, ChinaCenter of Applied Mathematics & Interdisciplinary Sciences, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430200, ChinaSchool of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, ChinaSchool of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, ChinaCenter of Applied Mathematics & Interdisciplinary Sciences, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430200, ChinaCenter of Applied Mathematics & Interdisciplinary Sciences, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430200, ChinaMore and more researchers use single-cell RNA sequencing (scRNA-seq) technology to characterize the transcriptional map at the single-cell level. They use it to study the heterogeneity of complex tissues, transcriptome dynamics, and the diversity of unknown organisms. However, there are generally lots of technical and biological noises in the scRNA-seq data since the randomness of gene expression patterns. These data are often characterized by high-dimension, sparsity, large number of “dropout” values, and affected by batch effects. A large number of “dropout” values in scRNA-seq data seriously conceal the important relationship between genes and hinder the downstream analysis. Therefore, the imputation of dropout values of scRNA-seq data is particularly important. We classify, analyze and compare the current advanced scRNA-seq data imputation methods from different angles. Through the comparison and analysis of the principle, advantages and disadvantages of the algorithm, it can provide suggestions for the selection of imputation methods for specific problems and diverse data, and have basic research significance for the downstream function analysis of data.https://www.mdpi.com/2076-3417/12/20/10684scRNA sequencingdropoutimputationdownstream analysis |
| spellingShingle | Mengyuan Wang Jiatao Gan Changfeng Han Yanbing Guo Kaihao Chen Ya-zhou Shi Ben-gong Zhang Imputation Methods for scRNA Sequencing Data Applied Sciences scRNA sequencing dropout imputation downstream analysis |
| title | Imputation Methods for scRNA Sequencing Data |
| title_full | Imputation Methods for scRNA Sequencing Data |
| title_fullStr | Imputation Methods for scRNA Sequencing Data |
| title_full_unstemmed | Imputation Methods for scRNA Sequencing Data |
| title_short | Imputation Methods for scRNA Sequencing Data |
| title_sort | imputation methods for scrna sequencing data |
| topic | scRNA sequencing dropout imputation downstream analysis |
| url | https://www.mdpi.com/2076-3417/12/20/10684 |
| work_keys_str_mv | AT mengyuanwang imputationmethodsforscrnasequencingdata AT jiataogan imputationmethodsforscrnasequencingdata AT changfenghan imputationmethodsforscrnasequencingdata AT yanbingguo imputationmethodsforscrnasequencingdata AT kaihaochen imputationmethodsforscrnasequencingdata AT yazhoushi imputationmethodsforscrnasequencingdata AT bengongzhang imputationmethodsforscrnasequencingdata |