Evaluation of Metrics for the Assessment of Microarray Data Quality

Peter Murakami
Morning Session, 3 September (11th MGED Meeting, 1-4 September, 2008)

They have created an algorithm with which to evaluate microarray data quality. Good quality assessment (QA) metrics can tell us to remove an array. This study makes use of a dataset of 5954 unique arrays from 167 studies from both GEO and ArrayExpress. Some quality metrics in common use when looking at the original image: average background, scale factor, and % genes called present by Affy's detection algorithm. RNA degradation metrics are also used.

They need arrays known to be of low quality by which to judge the success of the quality metrics. These were ones, for instance, when the RNA had been deliberately degraded in the lab, or used dog RNA rather than human. The arrays were identified using CAT and hierarchical cluster analysis using Euclidean distance based on gene expression estimates.

These are just my notes and are not guaranteed to be correct.
Please feel free to let me know about any errors, which are all my
fault and not the fault of the speaker. 🙂

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