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Toxicogenomics: Quality metrics for improving microarray based measurements

Toxicogenomics: Quality metrics for improving microarray based measurements. Carole Foy Daniel Hopkins LGC (in collaboration with EBI plus others) MGED8: September 2005. http://www.lgc.co.uk/ http://www.mfbprog.org.uk/ http://www.ebi.ac.uk/arrayexpress/. LGC

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Toxicogenomics: Quality metrics for improving microarray based measurements

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  1. Toxicogenomics: Quality metrics for improving microarray based measurements Carole Foy Daniel Hopkins LGC (in collaboration with EBI plus others) MGED8: September 2005 http://www.lgc.co.uk/ http://www.mfbprog.org.uk/ http://www.ebi.ac.uk/arrayexpress/

  2. LGC Designated UK National Measurement Institute for Chemical and Biochemical measurements Europe's largest independent analytical laboratoryproviding chemical, biochemical and DNA based analysis MfB Programme funded by UK Dept. Trade & Industry (DTI) to improve the accuracy, reliability and comparability of biomeasurements Strengthen measurement science in high priority technical areas where rapid developments in measurement technology are critical to their robust application and commercial exploitation Programme Outputs Measurement advice Good Practice Guides Standards & validation tools Training material International representation Key area: Gene measurements & DNA microarray standardisation: Improve confidence in microarray based measurements Guidance on minimising the impact of various sources of uncertainty on array data comparability Develop panel of quality metrics for assessment of array based measurements LGC & The Measurements for Biotechnology (MfB) Programme

  3. Phase 1 (‘01-’04) Array data from 5 platforms generated (Human Universal Reference and Brain RNA) : Phase 2 (‘04-’07) Generation of data using toxic model: Primary rat hepatocytes dosed with known toxins e.g. carbon tetrachloride Investigate RNA quality: ‘Good’ (RIN ~8), ‘Average’ (~6) & ‘Poor’ (~4) Up to 3 platforms compared, plus QRT-PCR Factors to be investigated: Replicate consistency (whole array and feature level) Array quality (whole array & individual features) Visual inspections Array QC checks (software specific) Spike-ins Filtering/flagging of features e.g. feature & background variation, features ‘not found’, SD of replicates, pixel saturation, detection threshold (80th percentile of control values) etc. Statistical assessment Platform concordance Quality Metrics and Microarray Standardisation • Factors investigated: • Platform comparability • Scanner • Image analysis software • Normalisation strategies • RNA starting amount

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