Name
Title | ||
|---|---|---|
A Multiple Comprehensive Analysis Of Scatac-Seq Based On Auto-Encoder And Matrix Decomposition |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/sym13081467 | SYMMETRY-BASEL |
Keywords | DocType | Volume |
autoencoder, matrix factorization, scATAC-seq | Journal | 13 |
Issue | Citations | PageRank |
8 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuyao Huang | 1 | 0 | 0.34 |
| Yizhou Li | 2 | 0 | 0.34 |
| Yuan Liu | 3 | 0 | 0.34 |
| Runyu Jing | 4 | 3 | 1.42 |
| Menglong Li | 5 | 94 | 11.85 |