A Multiple Comprehensive Analysis of scATAC-seq Based on Auto-Encoder and Matrix Decomposition
Single-cell ATAC-seq (scATAC-seq), as the updating of ATAC-seq, provides a novel method for probing open chromatin sites. Currently, research of scATAC-seq is faced with the problem of high dimensionality and the inherent sparsity of the generated data. Recently, several works proposed the use of an...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/8/1467 |
| _version_ | 1797521912736251904 |
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| author | Yuyao Huang Yizhou Li Yuan Liu Runyu Jing Menglong Li |
| author_facet | Yuyao Huang Yizhou Li Yuan Liu Runyu Jing Menglong Li |
| author_sort | Yuyao Huang |
| collection | DOAJ |
| description | Single-cell ATAC-seq (scATAC-seq), as the updating of ATAC-seq, provides a novel method for probing open chromatin sites. Currently, research of scATAC-seq is faced with the problem of high dimensionality and the inherent sparsity of the generated data. Recently, several works proposed the use of an autoencoder–decoder, a symmetry neural network architecture, and non-negative matrix factorization methods to characterize the high-dimensional data. To evaluate the performance of multiple methods, in this work, we performed a multiple comparison for characterizing scATAC-seq based on four kinds of auto-encoders known as a symmetry neural network, and two kinds of matrix factorization methods. Different sizes of latent features were used to generate the UMAP plots and for further K-means clustering. Using a gold-standard data set, we practically explored the performance among the methods and the number of latent features in a comprehensive way. Finally, we briefly discuss the underlying difficulties and future directions for scATAC-seq characterizing. As a result, the method designed for handling the sparsity outperforms other tools in the generated dataset. |
| first_indexed | 2024-03-10T08:20:11Z |
| format | Article |
| id | doaj.art-1dd8998636094f46bfb661a007927c87 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T08:20:11Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-1dd8998636094f46bfb661a007927c872023-11-22T10:01:59ZengMDPI AGSymmetry2073-89942021-08-01138146710.3390/sym13081467A Multiple Comprehensive Analysis of scATAC-seq Based on Auto-Encoder and Matrix DecompositionYuyao Huang0Yizhou Li1Yuan Liu2Runyu Jing3Menglong Li4College of Chemistry, Sichuan University, Chengdu 610064, ChinaSchool of Cyber Science and Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Chemistry, Sichuan University, Chengdu 610064, ChinaSchool of Cyber Science and Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Chemistry, Sichuan University, Chengdu 610064, ChinaSingle-cell ATAC-seq (scATAC-seq), as the updating of ATAC-seq, provides a novel method for probing open chromatin sites. Currently, research of scATAC-seq is faced with the problem of high dimensionality and the inherent sparsity of the generated data. Recently, several works proposed the use of an autoencoder–decoder, a symmetry neural network architecture, and non-negative matrix factorization methods to characterize the high-dimensional data. To evaluate the performance of multiple methods, in this work, we performed a multiple comparison for characterizing scATAC-seq based on four kinds of auto-encoders known as a symmetry neural network, and two kinds of matrix factorization methods. Different sizes of latent features were used to generate the UMAP plots and for further K-means clustering. Using a gold-standard data set, we practically explored the performance among the methods and the number of latent features in a comprehensive way. Finally, we briefly discuss the underlying difficulties and future directions for scATAC-seq characterizing. As a result, the method designed for handling the sparsity outperforms other tools in the generated dataset.https://www.mdpi.com/2073-8994/13/8/1467autoencodermatrix factorizationscATAC-seq |
| spellingShingle | Yuyao Huang Yizhou Li Yuan Liu Runyu Jing Menglong Li A Multiple Comprehensive Analysis of scATAC-seq Based on Auto-Encoder and Matrix Decomposition Symmetry autoencoder matrix factorization scATAC-seq |
| title | A Multiple Comprehensive Analysis of scATAC-seq Based on Auto-Encoder and Matrix Decomposition |
| title_full | A Multiple Comprehensive Analysis of scATAC-seq Based on Auto-Encoder and Matrix Decomposition |
| title_fullStr | A Multiple Comprehensive Analysis of scATAC-seq Based on Auto-Encoder and Matrix Decomposition |
| title_full_unstemmed | A Multiple Comprehensive Analysis of scATAC-seq Based on Auto-Encoder and Matrix Decomposition |
| title_short | A Multiple Comprehensive Analysis of scATAC-seq Based on Auto-Encoder and Matrix Decomposition |
| title_sort | multiple comprehensive analysis of scatac seq based on auto encoder and matrix decomposition |
| topic | autoencoder matrix factorization scATAC-seq |
| url | https://www.mdpi.com/2073-8994/13/8/1467 |
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