NDLI: A learned comparative expression measure for Affymetrix genechip DNA microarrays

Content Provider	IEEE Xplore Digital Library
Author	Sheffler, W. Upfal, E. Sedivy, J. Noble, W.S.
Copyright Year	2005
Description	Author affiliation: Dept. of Genome Sci., Washington Univ., MO, USA (Sheffler, W.)
Abstract	Perhaps the most common question that a microarray study can ask is, "Between two given biological conditions, which genes exhibit changed expression levels?" Existing methods for answering this question either generate a comparative measure based upon a static model, or take an indirect approach, first estimating absolute expression levels and then comparing the estimated levels to one another. We present a method for detecting changes in gene expression between two samples based on data from Affymetrix GeneChips. Using a library of over 200,000 known cases of differential expression, we create a learned comparative expression measure (LCEM) based on classification of probe-level data patterns as changed or unchanged. LCEM uses perfect match probe data only; mismatch probe values did not prove to be useful in this context. LCEM is particularly powerful in the case of small microarry studies, in which a regression-based method such as RMA cannot generalize, and in detecting small expression changes. At the levels of selectivity that are typical in microarray analysis, the LCEM shows a lower false discovery rate than either MAS5 or RMA trained from a single chip. When many chips are available to RMA, LCEM performs better on two out of the three data sets, and nearly as well on the third. Performance of the MAS5 log ratio statistic was notably bad on all datasets.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Bioinformatics
Starting Page	144
Ending Page	154
File Size	1972709
Page Count	11
File Format	PDF
ISBN	0769523447
DOI	10.1109/CSB.2005.5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genomics Gene expression Statistics microarrays Support vector machines Computer science Semiconductor device measurement support vector machine DNA Probes Bioinformatics gene expression Testing
Content Type	Text
Resource Type	Article

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