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

Access to Document

10.1093/bioinformatics/btn411

Cite this

@article{f6d3fe08448c4f6a93dfb08c0106c25c,

title = "Eigen-R2 for dissecting variation in high-dimensional studies",

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.",

author = "Chen, \{Lin S.\} and Storey, \{John D.\}",

year = "2008",

month = oct,

doi = "10.1093/bioinformatics/btn411",

language = "English (US)",

volume = "24",

pages = "2260--2262",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "19",

}

TY - JOUR

T1 - Eigen-R2 for dissecting variation in high-dimensional studies

AU - Chen, Lin S.

AU - Storey, John D.

PY - 2008/10

Y1 - 2008/10

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/52949114615

UR - https://www.scopus.com/pages/publications/52949114615#tab=citedBy

U2 - 10.1093/bioinformatics/btn411

DO - 10.1093/bioinformatics/btn411

M3 - Article

C2 - 18718946

AN - SCOPUS:52949114615

SN - 1367-4803

VL - 24

SP - 2260

EP - 2262

JO - Bioinformatics

JF - Bioinformatics

IS - 19

ER -

Eigen-R² for dissecting variation in high-dimensional studies

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this