Improving the default data analysis workflow for large autoimmune biomarker discovery studies with ProtoArrays. Issue 14 (20th June 2013)

• Record Type: Journal Article
• Authors: Turewicz, Michael; May, Caroline; Ahrens, Maike; Woitalla, Dirk; Gold, Ralf; Casjens, Swaantje; Pesch, Beate; Brüning, Thomas; Meyer, Helmut E.; Nordhoff, Eckhard; Böckmann, Miriam; Stephan, Christian; Eisenacher, Martin
• Is Part Of: Proteomics. Volume 13:Issue 14(2013:Jul.)
• Page Start: 2083
• Page End: 2087
• Publication Date: 2013-06-20
• Subjects: Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201200518
• Languages: English
• ISSNs: 1615-9853

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Contemporary protein microarrays such as 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 preselection method availability, batch effects, feature selection, and feature validation. In this work, appropriate improvements of the default workflow are proposed. 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.</p> </abstract>