Detecting outlier microarray arrays by correlation and percentage of outliers spots

Song Yang, Xiang Guo, Yaw Ching Yang, Denise Papcunik, Caroline Heckman, Jeffrey Hooke, Craig D. Shriver, Michael N. Liebman, Hai Hu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


We developed a quality assurance (QA) tool, namely microarray outlier filter (MOF), and have applied it to our microarray datasets for the identification of problematic arrays. Our approach is based on the comparison of the arrays using the correlation coefficient and the number of outlier spots generated on each array to reveal outlier arrays. For a human universal reference (HUR) dataset, which is used as a technical control in our standard hybridization procedure, 3 outlier arrays were identified out of 35 experiments. For a human blood dataset, 12 outlier arrays were identified from 185 experiments. In general, arrays from human blood samples displayed greater variation in their gene expression profiles than arrays from HUR samples. As a result, MOF identified two distinct patterns in the occurrence of outlier arrays. These results demonstrate that this methodology is a valuable QA practice to identify questionable microarray data prior to downstream analysis.

Original languageEnglish
Pages (from-to)351-360
Number of pages10
JournalCancer Informatics
StatePublished - 2006
Externally publishedYes


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