Bootstrap-based differential gene expression analysis for RNA-Seq data with and without replicates

被引:0
|
作者
Sahar Al Seesi
Yvette Temate Tiagueu
Alexander Zelikovsky
Ion I Măndoiu
机构
[1] University of Connecticut,Computer Science & Engineering Department
[2] Georgia State University,Computer Science Department
来源
BMC Genomics | / 15卷
关键词
differential gene expression; bootstrapping; RNA-Seq;
D O I
暂无
中图分类号
学科分类号
摘要
A major application of RNA-Seq is to perform differential gene expression analysis. Many tools exist to analyze differentially expressed genes in the presence of biological replicates. Frequently, however, RNA-Seq experiments have no or very few biological replicates and development of methods for detecting differentially expressed genes in these scenarios is still an active research area.
引用
收藏
相关论文
共 50 条
  • [21] Gene dispersion is the key determinant of the read count bias in differential expression analysis of RNA-seq data
    Yoon, Sora
    Nam, Dougu
    BMC GENOMICS, 2017, 18
  • [22] Power analysis for RNA-Seq differential expression studies
    Lianbo Yu
    Soledad Fernandez
    Guy Brock
    BMC Bioinformatics, 18
  • [23] Gene dispersion is the key determinant of the read count bias in differential expression analysis of RNA-seq data
    Sora Yoon
    Dougu Nam
    BMC Genomics, 18
  • [24] An iteration normalization and test method for differential expression analysis of RNA-seq data
    Yan Zhou
    Nan Lin
    Baoxue Zhang
    BioData Mining, 7
  • [25] An iteration normalization and test method for differential expression analysis of RNA-seq data
    Zhou, Yan
    Lin, Nan
    Zhang, Baoxue
    BIODATA MINING, 2014, 7
  • [26] Error estimates for the analysis of differential expression from RNA-seq count data
    Burden, Conrad J.
    Qureshi, Sumaira E.
    Wilson, Susan R.
    PEERJ, 2014, 2
  • [27] A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq Data
    Liu, Kefei
    Ye, Jieping
    Yang, Yang
    Shen, Li
    Jiang, Hui
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2019, 16 (02) : 442 - 454
  • [28] Empirical Bayes Analysis of RNA-seq Data for Detection of Gene Expression Heterosis
    Jarad Niemi
    Eric Mittman
    Will Landau
    Dan Nettleton
    Journal of Agricultural, Biological, and Environmental Statistics, 2015, 20 : 614 - 628
  • [29] Advantages of CEMiTool for gene co-expression analysis of RNA-seq data
    Cheng, Chew Weng
    Beech, David J.
    Wheatcroft, Stephen B.
    COMPUTERS IN BIOLOGY AND MEDICINE, 2020, 125
  • [30] Empirical Bayes Analysis of RNA-seq Data for Detection of Gene Expression Heterosis
    Niemi, Jarad
    Mittman, Eric
    Landau, Will
    Nettleton, Dan
    JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 2015, 20 (04) : 614 - 628