Detecting outlying samples in microarray data: A critical assessment of the effect of outliers on sample classification

Chem-Bio Informatics Journal

Katsutoshi Takahashi¹  Koji Kadota¹  Daisuke Tominaga¹  Yutaka Akiyama¹ 

[1] Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST)

关键词: outlier detection;    外れ値検出;    molecular classification;    サンプル分類;    DNA microarray;    DNAマイクロアレイ;    AIC;    赤池情報量規�?(AIC);    expression analysis;    遺伝子発現情報解析;   

DOI  :  10.1273/cbij.3.30

学科分类：生物化学/生物物理

来源: Chem-Bio Informatics Society

【 摘 要 】

References(36)Cited-By(7)Among samples analyzed for gene expression, samples incorrectly labeled or identified as likely contaminated are those whose expression patterns are markedly different. Such samples should be designated outliers, since they can exert a negative effect on the selection of informative genes for sample classification. We developed a method based on Akaike's Information Criterion (AIC) to detect such outliers. Our method is advantageous because it is free from a significance level and it facilitates objective decision-making. We applied our method to analyze the public microarray data of Alon et al. (1999) and found that some of the detected outlying samples coincided with samples considered as likely contaminated. Application of our method produced a higher discrimination level for informative genes in tumor- and normal tissues and, upon exclusion of the outliers, yielded higher classification accuracy. The detection of outlying samples prior to sample classification is essential, and the method described here serves as a valuable check.

【 授权许可】

Unknown