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Wiley, Proteomics, 14(13), p. 2083-2087, 2013

DOI: 10.1002/pmic.201200518

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Improving the default data analysis workflow for large autoimmune biomarker discovery studies with ProtoArrays

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

Contemporary protein microarrays like the ProtoArray® are used for autoimmune antibody screening studies to discover biomarker panels. For ProtoArray data analysis the software Prospector and a default workflow are suggested by the manufacturer. While analyzing a large data set of a discovery study for diagnostic biomarkers of the Parkinson's Disease ("ParkCHIP") we have revealed the need for distinct improvements of the suggested workflow concerning raw data acquisition, normalization and pre-selection method availability, batch effects, feature selection, and feature validation. In this work appropriate improvements of the default workflow are proposed and it is shown that completely automatic data acquisition as a batch, a re-implementation of Prospector's pre-selection method, multivariate or hybrid feature selection, and validation of the selected protein panel using an independent test set define in combination an improved workflow for large studies. This article is protected by copyright. All rights reserved.