A model-based factorization method for scRNA data unveils bifurcating transcriptional modules underlying cell fate determination

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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Bibliographic Details
Main Authors: Jun Ren, Ying Zhou, Yudi Hu, Jing Yang, Hongkun Fang, Xuejing Lyu, Jintao Guo, Xiaodong Shi, Qiyuan Li
Format: Article
Language:English
Published: eLife Sciences Publications Ltd 2025-02-01
Series:eLife
Subjects:
scRNA-seq
factorization
manifold-learning
bifurcation process
mixtures of Gaussian processes
Online Access:https://elifesciences.org/articles/97424
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https://elifesciences.org/articles/97424

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