Data Analysis Strategies for Protein Microarrays

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 char...

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Main Authors:	Díez, Paula, Dasilva, Noelia, González-González, María, Matarraz, Sergio, Casado-Vela, Juan, Orfao, Alberto, Fuentes, Manuel
Format:	Online
Language:	English
Published:	MDPI 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5003438/

id	pubmed-5003438
recordtype	oai_dc
spelling	pubmed-50034382016-09-06 Data Analysis Strategies for Protein Microarrays Díez, Paula Dasilva, Noelia González-González, María Matarraz, Sergio Casado-Vela, Juan Orfao, Alberto Fuentes, Manuel Review 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. MDPI 2012-08-06 /pmc/articles/PMC5003438/ /pubmed/27605336 http://dx.doi.org/10.3390/microarrays1020064 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0/ This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Díez, Paula Dasilva, Noelia González-González, María Matarraz, Sergio Casado-Vela, Juan Orfao, Alberto Fuentes, Manuel
spellingShingle	Díez, Paula Dasilva, Noelia González-González, María Matarraz, Sergio Casado-Vela, Juan Orfao, Alberto Fuentes, Manuel Data Analysis Strategies for Protein Microarrays
author_facet	Díez, Paula Dasilva, Noelia González-González, María Matarraz, Sergio Casado-Vela, Juan Orfao, Alberto Fuentes, Manuel
author_sort	Díez, Paula
title	Data Analysis Strategies for Protein Microarrays
title_short	Data Analysis Strategies for Protein Microarrays
title_full	Data Analysis Strategies for Protein Microarrays
title_fullStr	Data Analysis Strategies for Protein Microarrays
title_full_unstemmed	Data Analysis Strategies for Protein Microarrays
title_sort	data analysis strategies for protein microarrays
description	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.
publisher	MDPI
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5003438/
_version_	1613638288744120320

Data Analysis Strategies for Protein Microarrays

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