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Improving the default data analysis workflow for large autoimmune biomarker discovery studies with ProtoArrays
Author(s) -
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
Publication year - 2013
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201200518
Subject(s) - workflow , computer science , normalization (sociology) , feature selection , biomarker discovery , data mining , software , proteomics , artificial intelligence , database , biology , biochemistry , sociology , anthropology , gene , programming language
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.