NDLI: A simple strategy for detecting outlier samples in microarray data

Content Provider	IEEE Xplore Digital Library
Author	Xuesong Lu Yanda Li Xuegong Zhang
Copyright Year	2004
Description	Author affiliation: Dept. of Autom., Tsinghua Univ., Beijing, China (Xuesong Lu; Yanda Li; Xuegong Zhang)
Abstract	Microarrays can monitor expression levels of thousands of genes simultaneously. Many people have used the gene expression data obtained with microarrays to classify different groups of samples, such as different types or subtypes of cancers. In our experiments as well as those of some other investigators, it has been observed that in some microarray data sets, there might be outlier samples which are either caused by imperfectness in the experiments or by possible mislabeling at certain steps. The existence of such samples impacts classification accuracy and may even cause misleading conclusions. In this paper, we studied this problem with two simulated data sets of typical scenarios and formed a simple but powerful strategy for detecting such outlier or mislabeled samples, built upon cross validation of the basic SVM classifier. The strategy was applied to a public colon cancer data set and it successfully detected 6 outlier cases. This work suggests an effective scheme for detecting outlier samples in a data set and for evaluating the sample quality.
Starting Page	1331
Ending Page	1335
File Size	783569
Page Count	5
File Format	PDF
ISBN	0780386531
DOI	10.1109/ICARCV.2004.1469039
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Colon Gene expression Diseases Bioinformatics Automation Computerized monitoring Cancer detection Neoplasms
Content Type	Text
Resource Type	Article

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