DEGseq: an R package for identifying differentially expressed genes from RNA-seq data

被引:3259
|
作者
Wang, Likun [1 ,2 ,3 ]
Feng, Zhixing [1 ,2 ]
Wang, Xi [1 ,2 ]
Wang, Xiaowo [1 ,2 ]
Zhang, Xuegong [1 ,2 ]
机构
[1] Tsinghua Univ, MOE Key Lab Bioinformat, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Bioinformat Div, TNLIST Dept Automat, Beijing 100084, Peoples R China
[3] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
基金
中国国家自然科学基金;
关键词
MICROARRAYS; SINGLE;
D O I
10.1093/bioinformatics/btp612
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
High-throughput RNA sequencing (RNA-seq) is rapidly emerging as a major quantitative transcriptome profiling platform. Here, we present DEGseq, an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples. In this package, we integrated three existing methods, and introduced two novel methods based on MA-plot to detect and visualize gene expression difference.
引用
收藏
页码:136 / 138
页数:3
相关论文
共 50 条
  • [1] Variance component testing for identifying differentially expressed genes in RNA-seq data
    Yang, Sheng
    Shao, Fang
    Duan, Weiwei
    Zhao, Yang
    Chen, Feng
    [J]. PEERJ, 2017, 5
  • [2] Performances evaluation of algorithms for identifying differentially expressed genes in RNA-seq data
    Wu, Chin-Ting
    Tsai, Mong-Hsun
    Lu, Tzu-Pin
    Lai, Liang-Chuan
    Chuang, Eric Y.
    [J]. CANCER RESEARCH, 2015, 75
  • [3] Robust identification of differentially expressed genes from RNA-seq data
    Shahjaman, Md
    Mollah, Md Manir Hossain
    Rahman, Md Rezanur
    Islam, S. M. Shahinul
    Mollah, Md Nurul Haque
    [J]. GENOMICS, 2020, 112 (02) : 2000 - 2010
  • [4] An algorithm for identifying differentially expressed genes in multiclass RNA-seq samples
    An, Jaehyun
    Kim, Kwangsoo
    Kim, Sun
    [J]. 2014 INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP), 2014, : 40 - +
  • [5] Statistical methods for identifying differentially expressed genes in RNA-Seq experiments
    Zhide Fang
    Jeffrey Martin
    Zhong Wang
    [J]. Cell & Bioscience, 2
  • [6] Statistical methods for identifying differentially expressed genes in RNA-Seq exeriments
    Fang, Zhide
    Martin, Jeffrey
    Wang, Zhong
    [J]. CELL AND BIOSCIENCE, 2012, 2
  • [7] Identifying differentially expressed transcripts from RNA-seq data with biological variation
    Glaus, Peter
    Honkela, Antti
    Rattray, Magnus
    [J]. BIOINFORMATICS, 2012, 28 (13) : 1721 - 1728
  • [8] Robustness of single-cell RNA-seq for identifying differentially expressed genes
    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
    [J]. BMC Genomics, 24
  • [9] Identifying stably expressed genes from multiple RNA-Seq data sets
    Zhu, Bin
    Emerson, Sarah
    Chang, Jeff H.
    Di, Yanming
    [J]. PEERJ, 2016, 4
  • [10] Robustness of single-cell RNA-seq for identifying differentially expressed genes
    Liu, Yong
    Huang, Jing
    Pandey, Rajan
    Liu, Pengyuan
    Therani, Bhavika
    Qiu, Qiongzi
    Rao, Sridhar
    Geurts, Aron M.
    Cowley Jr, Allen W.
    Greene, Andrew S.
    Liang, Mingyu
    [J]. BMC GENOMICS, 2023, 24 (01)