Eigen-R2 for dissecting variation in high-dimensional studies

Lin S. Chen; John D. Storey

doi:10.1093/bioinformatics/btn411

Eigen-R² for dissecting variation in high-dimensional studies

Lin S. Chen, John D. Storey

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	2260-2262
Number of pages	3
Journal	Bioinformatics
Volume	24
Issue number	19
DOIs	https://doi.org/10.1093/bioinformatics/btn411
State	Published - Oct 2008

All Science Journal Classification (ASJC) codes

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

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10.1093/bioinformatics/btn411

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abstract = "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.",

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Eigen-R² for dissecting variation in high-dimensional studies

Abstract

All Science Journal Classification (ASJC) codes

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