alexa Statistical Analysis of Protein Microarray Data: A Case
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Review Article

Statistical Analysis of Protein Microarray Data: A Case Study in Type 1 Diabetes Research

Le TT An1-3#, Anna Pursiheimo1,2#, Robert Moulder1 and Laura L Elo1,2*

1Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland

2Department of Mathematics and Statistics, University of Turku, Finland

3School of Applied Mathematics and Informatics, Hanoi University of Science and Technology, Vietnam

#Authors contribute equally

*Corresponding Author:
Laura Elo
Adjunct Professor, Group Leader
Turku Centre for Biotechnology
and Department of Mathematics and Statistics
FI-20014 University of Turku, Finland
Tel: +358 2 333 8009
Fax: +358 2 231 8808
E-mail: [email protected]

Received date: September 14, 2014; Accepted date: October 24, 2014; Published date: October 28, 2014

Citation: An LTT, Pursiheimo A, Moulder R, Elo LL (2014) Statistical Analysis of Protein Microarray Data: A Case Study in Type 1 Diabetes Research. J Proteomics Bioinform S12:003. doi: 10.4172/jpb.S12-003

Copyright: © 2014 An LTT, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

In this report we provide an overview of protein microarrays and devote particular consideration on the statistical methods used in data analysis with applications concerning the study of type 1 diabetes. The latter methodologies are illustrated with publically available data from a study that identified novel type 1 diabetes associated autoantibodies. Amongst the methods employed, Reproducibility-Optimized Test Statistic (ROTS) shows better detection over the widely used LIMMA. With the application of this analytical approach, we identify new protein biomarkers that were not previously reported in original investigation. This observation emphasises the benefit of using different methods to extract critical information in the analysis of microarray data.

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