scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data Article Swipe
Zeyu Fu
,
Song Wang
,
Kangfu Sun
,
Baichuan Xu
,
Xianpeng Ye
,
Zhaoyang Wen
,
Mingqiang Shen
,
Chen Mo
,
Fang Chen
,
Yang Xu
,
Youcai Deng
,
Junping Wang
,
Shilei Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.07.04.602019
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.07.04.602019
The rapid development of single-cell sequencing offers an unparalleled opportunity to delineate the heterogeneity of individual cells. However, current methods struggle to pinpoint the states of cell fate decisions. In this study, we introduce a novel approach called Single-cell Reinforcement Learning (scRL), which integrates reinforcement learning into single-cell data analysis using an actor-critic architecture. Among existing dimensionality reduction methods, we identified one with the best interpretability. Based on this latent space and combined with our scRL algorithm, we assessed the intensity of fate decisions at the single-cell level. Extensive evaluations demonstrate that scRL outperforms existing techniques, as well as their variants and alternative approaches, in assessing cell fate decisions. Moreover, scRL offers an alternative method for evaluating the intensity of cell lineage differentiation which shows competitive interpretability as well. The superiority of scRL in assessing fate decisions is confirmed across several types of single-cell datasets.
Related Topics
Concepts
Cell fate determination
Reinforcement learning
Biology
Lineage (genetic)
Cell
Haematopoiesis
Reinforcement
Single-cell analysis
Computational biology
Hematopoietic cell
Cellular differentiation
Computer science
Stem cell
Gene
Genetics
Artificial intelligence
Transcription factor
Psychology
Social psychology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.07.04.602019
- OA Status
- green
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400477055
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400477055Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2024.07.04.602019Digital Object Identifier
- Title
-
scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell DataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-08Full publication date if available
- Authors
-
Zeyu Fu, Song Wang, Kangfu Sun, Baichuan Xu, Xianpeng Ye, Zhaoyang Wen, Mingqiang Shen, Chen Mo, Fang Chen, Yang Xu, Youcai Deng, Junping Wang, Shilei ChenList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.07.04.602019Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1101/2024.07.04.602019Direct OA link when available
- Concepts
-
Cell fate determination, Reinforcement learning, Biology, Lineage (genetic), Cell, Haematopoiesis, Reinforcement, Single-cell analysis, Computational biology, Hematopoietic cell, Cellular differentiation, Computer science, Stem cell, Gene, Genetics, Artificial intelligence, Transcription factor, Psychology, Social psychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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