Eigen-R2 for dissecting variation in high-dimensional studies

Lin S. Chen, John D. Storey

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


We provide a new statistical algorithm and software package called 'eigen-R2' for dissecting the variation of a high-dimensional biological dataset with respect to other measured variables of interest. We apply eigen-R2 to two real-life examples and compare it with simply averaging R2 over many features.

Original languageEnglish (US)
Pages (from-to)2260-2262
Number of pages3
Issue number19
StatePublished - Oct 2008

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Molecular Biology
  • Biochemistry
  • Statistics and Probability
  • Computer Science Applications
  • Computational Theory and Mathematics


Dive into the research topics of 'Eigen-R2 for dissecting variation in high-dimensional studies'. Together they form a unique fingerprint.

Cite this