Combining Global-Constrained Concept Factorization and a Regularized Gaussian Graphical Model for Clustering Single-Cell RNA-seq Data-Reference-Cited by-同舟云学术

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Combining Global-Constrained Concept Factorization and a Regularized Gaussian Graphical Model for Clustering Single-Cell RNA-seq Data

Published:2023-10-10 Issue: Volume: Page:
ISSN:1913-2751
Container-title:Interdisciplinary Sciences: Computational Life Sciences
language:en
Short-container-title:Interdiscip Sci Comput Life Sci

Author:

Xu Yaxin,Zhang Wei^ORCID,Zheng Xiaoying,Cai Xianxian

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangxi Province

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Computer Science Applications,General Biochemistry, Genetics and Molecular Biology

Link

https://link.springer.com/content/pdf/10.1007/s12539-023-00587-7.pdf

Reference62 articles.

1. Liu Z, Liu F, Hong C et al (2019) Detection of cell types from single-cell RNA-seq data using similarity via kernel preserving learning embedding. IEEE Int Conf Bioinf Biomed 2019:451–457. https://doi.org/10.1109/BIBM47256.2019.8983395

2. Ding S, Wang D, Zhou X et al (2022) Predicting heart cell types by using transcriptome profiles and a machine learning method. Life-Basel 12(2):228. https://doi.org/10.3390/life12020228

3. Zhou X, Ding S, Wang D et al (2022) Identification of cell markers and their expression patterns in skin based on single-cell RNA-sequencing profiles. Life-Basel 12(4):550. https://doi.org/10.3390/life12040550

4. Wen L, Li G, Huang T et al (2022) Single-cell technologies: from research to application. Innovation 36(5):100342. https://doi.org/10.1016/j.xinn.2022.100342

5. Wen L, Tang F (2018) Boosting the power of single-cell analysis. Nat Biotechnol 36(5):408–409. https://doi.org/10.1038/nbt.4131

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