Detection of Cell Separation-Induced Gene Expression Through a Penalized Deconvolution Approach-Reference-Cited by-同舟云学术

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Detection of Cell Separation-Induced Gene Expression Through a Penalized Deconvolution Approach

Published:2022-05-20 Issue: Volume: Page:
ISSN:1867-1764
Container-title:Statistics in Biosciences
language:en
Short-container-title:Stat Biosci

Author:

Tai An-Shun,Wang Chun-Chao,Hsieh Wen-Ping^ORCID

Funder

Ministry of Science and Technology, Taiwan

Publisher

Springer Science and Business Media LLC

Subject

Biochemistry, Genetics and Molecular Biology (miscellaneous),Statistics and Probability

Link

https://link.springer.com/content/pdf/10.1007/s12561-022-09344-8.pdf

Reference44 articles.

1. Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12:87

2. Stegle O, Teichmann SA, Marioni JC (2015) Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet 16:133

3. Bacher R, Kendziorski C (2016) Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol 17:63

4. Brennecke P et al (2013) Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods 10:1093

5. Finak G et al (2015) MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol 16:278

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1. Unlocking Cellular Insights Through Cell-Type Decomposition;Statistics in Biosciences;2023-10-04

2. TGFBR3 supports anoikis through suppressing ATF4 signaling;Journal of Cell Science;2022-08-30

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