Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots
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 gen...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2006-01-01
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| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.1177/117693510600200017 |
| _version_ | 1818210513127997440 |
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| author | Song Yang Xiang Guo Yaw-Ching Yang Denise Papcunik Caroline Heckman Jeffrey Hooke Craig D. Shriver Michael N. Liebman Hai Hu |
| author_facet | Song Yang Xiang Guo Yaw-Ching Yang Denise Papcunik Caroline Heckman Jeffrey Hooke Craig D. Shriver Michael N. Liebman Hai Hu |
| author_sort | Song Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T05:17:48Z |
| format | Article |
| id | doaj.art-f1794b9e54c74dc4806e00e277b91887 |
| institution | Directory Open Access Journal |
| issn | 1176-9351 |
| language | English |
| last_indexed | 2024-12-12T05:17:48Z |
| publishDate | 2006-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj.art-f1794b9e54c74dc4806e00e277b918872022-12-22T00:36:44ZengSAGE PublishingCancer Informatics1176-93512006-01-01210.1177/117693510600200017Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers SpotsSong Yang0Xiang Guo1Yaw-Ching Yang2Denise Papcunik3Caroline Heckman4Jeffrey Hooke5Craig D. Shriver6Michael N. Liebman7Hai Hu8Windber Research Institute, Windber, PA.Windber Research Institute, Windber, PA.Windber Research Institute, Windber, PA.Windber Research Institute, Windber, PA.Windber Research Institute, Windber, PA.Walter Reed Army Medical Center, Washington DC.Walter Reed Army Medical Center, Washington DC.Windber Research Institute, Windber, PA.Windber Research Institute, Windber, PA.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.https://doi.org/10.1177/117693510600200017 |
| spellingShingle | Song Yang Xiang Guo Yaw-Ching Yang Denise Papcunik Caroline Heckman Jeffrey Hooke Craig D. Shriver Michael N. Liebman Hai Hu Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots Cancer Informatics |
| title | Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots |
| title_full | Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots |
| title_fullStr | Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots |
| title_full_unstemmed | Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots |
| title_short | Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots |
| title_sort | detecting outlier microarray arrays by correlation and percentage of outliers spots |
| url | https://doi.org/10.1177/117693510600200017 |
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