Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots
| Autor: | Song Yang, Xiang Guo, Yaw-Ching Yang, Denise Papcunik, Caroline Heckman, Jeffrey Hooke, Craig D. Shriver, Michael N. Liebman, Hai Hu |
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| Jazyk: | angličtina |
| Rok vydání: | 2007 |
| Předmět: |
0301 basic medicine
Cancer Research lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens lcsh:RC254-282 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Oncology 030220 oncology & carcinogenesis parasitic diseases population characteristics geographic locations health care economics and organizations Original Research |
| Zdroj: | Cancer Informatics Cancer Informatics, Vol 2, Pp 351-360 (2006) Cancer Informatics, Vol 2 (2006) |
| ISSN: | 1176-9351 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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