Identifying Cell Subpopulations and Their Genetic Drivers from Single-Cell RNA-Seq Data Using a Biclustering Approach-Reference-Cited by-同舟云学术

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Identifying Cell Subpopulations and Their Genetic Drivers from Single-Cell RNA-Seq Data Using a Biclustering Approach

Published:2017-07 Issue:7 Volume:24 Page:663-674
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Shi Funan¹,Huang Haiyan¹

Affiliation:

1. Department of Statistics, University of California, Berkeley, California.

Publisher

Mary Ann Liebert Inc

Subject

Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modeling and Simulation

Link

http://www.liebertpub.com/doi/pdf/10.1089/cmb.2017.0049

Reference23 articles.

1. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

2. Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells

3. STAR: ultrafast universal RNA-seq aligner

4. Human housekeeping genes, revisited

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression;Cell Reports Methods;2024-04

2. Single-cell RNA-seq data analysis based on directed graph neural network;Methods;2023-03

3. Single-Cell Biclustering for Cell-Specific Transcriptomic Perturbation Detection in AD Progression;2023

4. Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks;Frontiers in Genetics;2022-12-09

5. An Effective Biclustering-Based Framework for Identifying Cell Subpopulations From scRNA-seq Data;IEEE/ACM Transactions on Computational Biology and Bioinformatics;2021-11-01

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