Robustness of single-cell RNA-seq for identifying differentially expressed genes

被引:0
|
作者
Yong Liu
Jing Huang
Rajan Pandey
Pengyuan Liu
Bhavika Therani
Qiongzi Qiu
Sridhar Rao
Aron M. Geurts
Allen W. Cowley
Andrew S. Greene
Mingyu Liang
机构
[1] Medical College of Wisconsin, Department of Physiology, Center of Systems Molecular Medicine
[2] University of Arizona College of Medicine – Tucson,Department of Physiology
[3] Sir Run Run Shaw Hospital,Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province
[4] Zhejiang University School of Medicine,Cancer Center
[5] Zhejiang University,Institute of Translational Medicine
[6] Zhejiang University School of Medicine,Department of Cell Biology, Neurobiology, and Anatomy
[7] Versiti Blood Research Institute,Division of Pediatric Hematology/Oncology/Transplantation
[8] Medical College of Wisconsin,undefined
[9] Medical College of Wisconsin,undefined
[10] The Jackson Laboratory,undefined
来源
BMC Genomics | / 24卷
关键词
RNA-seq; Gene expression; Stem cell; Single cell;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 50 条
  • [41] Reproducibility of Methods to Detect Differentially Expressed Genes from Single-Cell RNA Sequencing
    Mou, Tian
    Deng, Wenjiang
    Gu, Fengyun
    Pawitan, Yudi
    Trung Nghia Vu
    FRONTIERS IN GENETICS, 2020, 10
  • [42] Evaluating imputation methods for single-cell RNA-seq data
    Yi Cheng
    Xiuli Ma
    Lang Yuan
    Zhaoguo Sun
    Pingzhang Wang
    BMC Bioinformatics, 24
  • [43] Accounting for technical noise in single-cell RNA-seq experiments
    Brennecke, Philip
    Anders, Simon
    Kim, Jong Kyoung
    Kolodziejczyk, Aleksandra A.
    Zhang, Xiuwei
    Proserpio, Valentina
    Baying, Bianka
    Benes, Vladimir
    Teichmann, Sarah A.
    Marioni, John C.
    Heisler, Marcus G.
    NATURE METHODS, 2013, 10 (11) : 1093 - 1095
  • [44] Exponential scaling of single-cell RNA-seq in the past decade
    Svensson, Valentine
    Vento-Tormo, Roser
    Teichmann, Sarah A.
    NATURE PROTOCOLS, 2018, 13 (04) : 599 - 604
  • [45] Determining sequencing depth in a single-cell RNA-seq experiment
    Zhang, Martin Jinye
    Ntranos, Vasilis
    Tse, David
    NATURE COMMUNICATIONS, 2020, 11 (01)
  • [46] Challenges in unsupervised clustering of single-cell RNA-seq data
    Kiselev, Vladimir Yu
    Andrews, Tallulah S.
    Hemberg, Martin
    NATURE REVIEWS GENETICS, 2019, 20 (05) : 273 - 282
  • [47] Evaluating imputation methods for single-cell RNA-seq data
    Cheng, Yi
    Ma, Xiuli
    Yuan, Lang
    Sun, Zhaoguo
    Wang, Pingzhang
    BMC BIOINFORMATICS, 2023, 24 (01)
  • [48] Advances and challenges in single-cell RNA-seq of microbial communities
    Imdahl, Fabian
    Saliba, Antoine-Emmanuel
    CURRENT OPINION IN MICROBIOLOGY, 2020, 57 : 102 - 110
  • [49] Single-cell RNA-seq clustering: datasets, models, and algorithms
    Peng, Lihong
    Tian, Xiongfei
    Tian, Geng
    Xu, Junlin
    Huang, Xin
    Weng, Yanbin
    Yang, Jialiang
    Zhou, Liqian
    RNA BIOLOGY, 2020, 17 (06) : 765 - 783
  • [50] PsiNorm: a scalable normalization for single-cell RNA-seq data
    Borella, Matteo
    Martello, Graziano
    Risso, Davide
    Romualdi, Chiara
    BIOINFORMATICS, 2022, 38 (01) : 164 - 172