NDLI: Integrating Affymetrix microarray data sets using probe-level test statistic for predicting prostate cancer

Content Provider	IEEE Xplore Digital Library
Author	Pingzhao Hu Greenwood, C.M.T. Beyene, J.
Copyright Year	2006
Description	Author affiliation: Program in Genetics & Genomic Biol., Hosp. for Sick Children Res. Inst., Toronto, Ont. (Pingzhao Hu; Greenwood, C.M.T.)
Abstract	Microarray technology has previously been used to identify differentially expressed genes between tumor and normal prostate samples in a single study as well as in a synthesis involving multiple studies. When integrating results from several Affymetrix microarray datasets, previous studies have used probeset-level data which may lead to a loss of information contained at the probe-level. Here, we propose a new approach for combining results across studies, based on a probe-level test statistic. Each probe-level test statistic is transformed into an effect size measure for each probeset and a random-effects model (REM) is used to integrate effect sizes across studies. We compared statistical and biological significance of the prognostic gene expression signatures identified in the probe-level model (PLM) with those in the probeset-level model (PSLM). Support vector machines (SVMs)-based predictive models were built using these two sets of signatures and their performances were evaluated using independent test datasets. Our analyses show that the prognostic gene expression signatures identified through the probe-level test statistics are more strongly differentially expressed and have better prediction accuracy than signatures derived from a probeset-level model
Starting Page	1
Ending Page	8
File Size	225717
Page Count	8
File Format	PDF
ISBN	1424406234
DOI	10.1109/CIBCB.2006.331013
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-28
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Testing Statistical analysis Prostate cancer Biological system modeling Gene expression Predictive models Neoplasms Size measurement Support vector machines Performance evaluation
Content Type	Text
Resource Type	Article

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