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2012 ; 1
(2
): 64-83
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Data Analysis Strategies for Protein Microarrays
#MMPMID27605336
Díez P
; Dasilva N
; González-González M
; Matarraz S
; Casado-Vela J
; Orfao A
; Fuentes M
Microarrays (Basel)
2012[Aug]; 1
(2
): 64-83
PMID27605336
show ga
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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