A model-based factorization method for scRNA data unveils bifurcating transcriptional modules underlying cell fate determination
Manifold-learning is particularly useful to resolve the complex cellular state space from single-cell RNA sequences. While current manifold-learning methods provide insights into cell fate by inferring graph-based trajectory at cell level, challenges remain to retrieve interpretable biology underlyi...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
eLife Sciences Publications Ltd
2025-02-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/97424 |
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