Discrete latent embeddings illuminate cellular diversity in single-cell epigenomics

Zhi Wei

doi:10.1038/s43588-024-00634-3

Discrete latent embeddings illuminate cellular diversity in single-cell epigenomics

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

CASTLE, a deep learning approach, extracts interpretable discrete representations from single-cell chromatin accessibility data, enabling accurate cell type identification, effective data integration, and quantitative insights into gene regulatory mechanisms.

Original language	English (US)
Pages (from-to)	316-317
Number of pages	2
Journal	Nature Computational Science
Volume	4
Issue number	5
DOIs	https://doi.org/10.1038/s43588-024-00634-3
State	Published - May 2024
Externally published	Yes

All Science Journal Classification (ASJC) codes

Computer Science (miscellaneous)
Computer Science Applications
Computer Networks and Communications

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10.1038/s43588-024-00634-3

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title = "Discrete latent embeddings illuminate cellular diversity in single-cell epigenomics",

abstract = "CASTLE, a deep learning approach, extracts interpretable discrete representations from single-cell chromatin accessibility data, enabling accurate cell type identification, effective data integration, and quantitative insights into gene regulatory mechanisms.",

author = "Zhi Wei",

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year = "2024",

month = may,

doi = "10.1038/s43588-024-00634-3",

language = "English (US)",

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Discrete latent embeddings illuminate cellular diversity in single-cell epigenomics

Abstract

All Science Journal Classification (ASJC) codes

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