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Data Analysis Strategies for Protein Microarrays
1
Centro de Investigación del Cáncer/IBMCC (USAL/CSIC), IBSAL, Departamento de Medicina and Servicio General de Citometría, University of Salamanca, Salamanca 37007, Spain
2
Translational Oncology Unit, Instituto de Investigaciones Biomédicas ‘Alberto Sols’, Spanish National Research Council (CSIC-UAM), 28029 Madrid, Spain
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Microarrays 2012, 1(2), 64-83; https://doi.org/10.3390/microarrays1020064
Received: 13 June 2012
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Revised: 13 July 2012
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Accepted: 31 July 2012
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Published: 6 August 2012
(This article belongs to the Special Issue Feature Papers)
Microarrays constitute a new platform which allows the discovery and characterization of proteins. According to different features, such as content, surface or detection system, there are many types of protein microarrays which can be applied for the identification of disease biomarkers and the characterization of protein expression patterns. However, the analysis and interpretation of the amount of information generated by microarrays remain a challenge. Further data analysis strategies are essential to obtain representative and reproducible results. Therefore, the experimental design is key, since the number of samples and dyes, among others aspects, would define the appropriate analysis method to be used. In this sense, several algorithms have been proposed so far to overcome analytical difficulties derived from fluorescence overlapping and/or background noise. Each kind of microarray is developed to fulfill a specific purpose. Therefore, the selection of appropriate analytical and data analysis strategies is crucial to achieve successful biological conclusions. In the present review, we focus on current algorithms and main strategies for data interpretation.
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Keywords:
microarray; proteome; biomarker; algorithm; normalization; fluorescence intensity; background correction
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This is an open access article distributed under the Creative Commons Attribution License.
MDPI and ACS Style
Díez, P.; Dasilva, N.; González-González, M.; Matarraz, S.; Casado-Vela, J.; Orfao, A.; Fuentes, M. Data Analysis Strategies for Protein Microarrays. Microarrays 2012, 1, 64-83. https://doi.org/10.3390/microarrays1020064
AMA Style
Díez P, Dasilva N, González-González M, Matarraz S, Casado-Vela J, Orfao A, Fuentes M. Data Analysis Strategies for Protein Microarrays. Microarrays. 2012; 1(2):64-83. https://doi.org/10.3390/microarrays1020064
Chicago/Turabian StyleDíez, Paula, Noelia Dasilva, María González-González, Sergio Matarraz, Juan Casado-Vela, Alberto Orfao, and Manuel Fuentes. 2012. "Data Analysis Strategies for Protein Microarrays" Microarrays 1, no. 2: 64-83. https://doi.org/10.3390/microarrays1020064
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