Evaluation of an Affymetrix High-density Oligonucleotide Microarray Platform as a Measurement System

Heuvel, E. R. V. D., Geeven, G., Bauerschmidt, S. & Polman, J. E. M., 2005, In : Quality and Reliability Engineering International. p. 491-508 18 p.

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  • EvaluationofanAffymetrixHigh-density OligonucleotideMicroarray Platform as aMeasurement System

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  • Edwin R. van den Heuvel
  • Geert Geeven
  • Susanne Bauerschmidt
  • Jan E.M. Polman
The random measurement or technological variation of an Affymetrix high-density oligonucleotide microarray platform is estimated and evaluated against the variation in the gene expression levels of a biological sample. A mixed effects analysis of variance model is used to describe the probe intensity levels. The focus is on the repeatability of the method. Additionally, the minimal number of arrays necessary to detect biological relevant differences in gene expression levels between biological samples is estimated based on the repeatability. The investigated sources of variation for the repeatability are arrays, array production lots, the reverse transcription step, the in vitro transcription step and the hybridization and washing step. They may all add to the short-term random measurement variation. The effect of global linear normalization on the repeatability is determined. It is shown that the in vitro transcription step contributes to the repeatability with more than 50%. If normalization is used, the microarray platform is indeed suitable for measuring thousands of genes simultaneously and in particular to detect differences in gene expression between biological samples. Based on a goodness-of-fit investigation and considering the amount of data involved, the analysis of variance model seems suitable to describe the raw probe intensity data.
Original languageEnglish
Pages (from-to)491-508
Number of pages18
JournalQuality and Reliability Engineering International
Publication statusPublished - 2005


  • repeatability, probe intensity levels, precision, high-density oligonucleotide microarray, measurement system analysis, mixed effects analysis of variance model, gene expression

ID: 14404166