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Bioinformatics Vol. 18 no. 90001 2002
Pages S155-S163
© 2002 Oxford University Press

Statistical process control for large scale microarray experiments

Fabian Model , Thomas König , Christian Piepenbrock and Péter Adorján

Epigenomics AG, Kastanienallee 24, Berlin, D-10435, Germany

Received on January 24, 2002 ; revised on March 28, 2002 ; accepted on March 28, 2002

Motivation: Maintaining and controlling data quality is a key problem in large scale microarray studies. In particular systematic changes in experimental conditions across multiple chips can seriously affect quality and even lead to false biological conclusions. Traditionally the influence of these effects can be minimized only by expensive repeated measurements, because a detailed understanding of all process relevant parameters seems impossible.

Results: We introduce a novel method for microarray process control that estimates quality based solely on the distribution of the actual measurements without requiring repeated experiments. A robust version of principle component analysis detects single outlier microarrays and thereby enables the use of techniques from multivariate statistical process control. In particular, the T2 control chart reliably tracks undesired changes in process relevant parameters. This can be used to improve the microarray process itself, limits necessary repetitions to only affected samples and therefore maintains quality in a cost effective way. We prove the power of the approach on 3 large sets of DNA methylation microarray data.

Contact: Fabian.Model{at}epigenomics.com

Keywords: DNA methylation; robust PCA; micorarray; statistical process control; quality control.


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