SCALE method for single-cell ATAC-seq analysis via latent feature extraction
Single-cell ATAC-seq data is challenging to analyse for reasons such as high dimensionality and sparsity. Here, the authors develop SCALE, a deep learning method that leverages latent feature extraction for various tasks of scATACseq data analysis.
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2019
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/9307133826f6483aacbf4da911ec0cc4 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Single-cell ATAC-seq data is challenging to analyse for reasons such as high dimensionality and sparsity. Here, the authors develop SCALE, a deep learning method that leverages latent feature extraction for various tasks of scATACseq data analysis. |
|---|