Journal article
Robust semiparametric microarray normalization and significance analysis
Biometrics, Vol.62(2), pp.555-561
06/2006
DOI: 10.1111/j.1541-0420.2005.00452.x
PMID: 16918920
Abstract
Microarray technology allows the monitoring of expression levels of thousands of genes simultaneously. A semiparametric location and scale model is proposed to model gene expression levels for normalization and significance analysis purposes. Robust estimation based on weighted least absolute deviation regression and significance analysis based on the weighted bootstrap are investigated. The proposed approach naturally combines normalization and significance analysis, and incorporates the variations due to normalization into the significance analysis properly. A small simulation study is used to compare finite sample performance of the proposed approach with alternatives. We also demonstrate the proposed method with a real dataset.
Details
- Title: Subtitle
- Robust semiparametric microarray normalization and significance analysis
- Creators
- Shuangge Ma - University of WashingtonMichael R Kosorok - University of Wisconsin–MadisonJian Huang - University of IowaHehuang Xie - Northwestern UniversityLiliana Manzella - Northwestern UniversityMarcelo Bento Soares - Northwestern University
- Resource Type
- Journal article
- Publication Details
- Biometrics, Vol.62(2), pp.555-561
- DOI
- 10.1111/j.1541-0420.2005.00452.x
- PMID
- 16918920
- ISSN
- 0006-341X
- eISSN
- 1541-0420
- Grant note
- HL72288-01 / NHLBI NIH HHS CA75142 / NCI NIH HHS
- Language
- English
- Date published
- 06/2006
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257611702771
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