A Clustering Method for Single-Cell RNA-Seq Data Based on Automatic Weighting Penalty and Low-Rank Representation-Reference-Cited by-同舟云学术

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A Clustering Method for Single-Cell RNA-Seq Data Based on Automatic Weighting Penalty and Low-Rank Representation

Published:2024-05 Issue:3 Volume:21 Page:360-371
ISSN:1545-5963
Container-title:IEEE/ACM Transactions on Computational Biology and Bioinformatics
language:
Short-container-title:IEEE/ACM Trans. Comput. Biol. and Bioinf.

Author:

Wang Juan¹^ORCID,Wang Zhenchang¹^ORCID,Yuan Shasha¹^ORCID,Zheng Chunhou¹^ORCID,Liu Jinxing¹^ORCID,Shang Junliang¹^ORCID

Affiliation:

1. School of Computer Science, Qufu Normal University, Rizhao, China

Funder

National Natural Science Foundation of China

University of Shandong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8857/10550082/10423184.pdf?arnumber=10423184

Reference49 articles.

1. RCA2: a scalable supervised clustering algorithm that reduces batch effects in scRNA-seq data

2. Library preparation for next generation sequencing: A review of automation strategies

3. Global characterization of B cell receptor repertoire in COVID-19 patients by single-cell V(D)J sequencing

4. Application of single-cell sequencing technologies in pancreatic cancer

5. Single-cell RNA-sequencing reports growth-condition-specific global transcriptomes of individual bacteria

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