A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder
Abstract Dimensionality reduction is crucial for the visualization and interpretation of the high-dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving topological structure among cells to low dimensional space remains a challenge. Here, we present the single-cell graph autoen...
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| Auteurs principaux: | , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/30dec251c0cd4590a65a1c8076f91bcf |
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