Super-sparse principal component analyses for high-throughput genomic data

被引:0
|
作者
Donghwan Lee
Woojoo Lee
Youngjo Lee
Yudi Pawitan
机构
[1] Seoul National University,Department of Statistics
[2] Karolinska Institutet,Department of Medical Epidemiology and Biostatistics
来源
BMC Bioinformatics | / 11卷
关键词
Principal Component Analysis; Lasso; Singular Vector; Sample Covariance Matrix; Principal Component Analysis Method;
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
  • [1] Super-sparse principal component analyses for high-throughput genomic data
    Lee, Donghwan
    Lee, Woojoo
    Lee, Youngjo
    Pawitan, Yudi
    BMC BIOINFORMATICS, 2010, 11
  • [2] A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis
    Reese, Sarah E.
    Archer, Kellie J.
    Therneau, Terry M.
    Atkinson, Elizabeth J.
    Vachon, Celine M.
    de Andrade, Mariza
    Kocher, Jean-Pierre A.
    Eckel-Passow, Jeanette E.
    BIOINFORMATICS, 2013, 29 (22) : 2877 - 2883
  • [3] High-Throughput Genomic Data in Systematics and Phylogenetics
    Lemmon, Emily Moriarty
    Lemmon, Alan R.
    ANNUAL REVIEW OF ECOLOGY, EVOLUTION, AND SYSTEMATICS, VOL 44, 2013, 44 : 99 - +
  • [4] Sparse Canonical Covariance Analysis for High-throughput Data
    Lee, Woojoo
    Lee, Donghwan
    Lee, Youngjo
    Pawitan, Yudi
    STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, 2011, 10 (01)
  • [5] Incorporating biological information in sparse principal component analysis with application to genomic data
    Ziyi Li
    Sandra E. Safo
    Qi Long
    BMC Bioinformatics, 18
  • [6] Incorporating biological information in sparse principal component analysis with application to genomic data
    Li, Ziyi
    Safo, Sandra E.
    Long, Qi
    BMC BIOINFORMATICS, 2017, 18
  • [7] Detecting selection in low-coverage high-throughput sequencing data using principal component analysis
    Meisner, Jonas
    Albrechtsen, Anders
    Hanghoj, Kristian
    BMC BIOINFORMATICS, 2021, 22 (01)
  • [8] Detecting selection in low-coverage high-throughput sequencing data using principal component analysis
    Jonas Meisner
    Anders Albrechtsen
    Kristian Hanghøj
    BMC Bioinformatics, 22
  • [9] Principal component analysis for sparse high-dimensional data
    Raiko, Tapani
    Ilin, Alexander
    Karhunen, Juha
    NEURAL INFORMATION PROCESSING, PART I, 2008, 4984 : 566 - 575
  • [10] Classification for high-throughput data with an optimal subset of principal components
    Song, Joon Jin
    Ren, Yuan
    Yan, Fenglan
    COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2009, 33 (05) : 408 - 413