Microarrays 2013, 2(2), 34-50; doi:10.3390/microarrays2020034

Challenges for MicroRNA Microarray Data Analysis

1email and 2,* email
1 Department of Mathematics and Statistics, University of South Alabama, 411 University BLVD N,Room 325, Mobile, AL 36688, USA 2 Mitchell Cancer Institute, University of South Alabama, 1660 Springhill Avenue, Mobile, AL 36604,USA
* Author to whom correspondence should be addressed.
Received: 20 February 2013; in revised form: 18 March 2013 / Accepted: 21 March 2013 / Published: 25 March 2013
(This article belongs to the Special Issue MicroRNA Microarrays)
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Abstract: Microarray is a high throughput discovery tool that has been broadly used for genomic research. Probe-target hybridization is the central concept of this technology to determine the relative abundance of nucleic acid sequences through fluorescence-based detection. In microarray experiments, variations of expression measurements can be attributed to many different sources that influence the stability and reproducibility of microarray platforms. Normalization is an essential step to reduce non-biological errors and to convert raw image data from multiple arrays (channels) to quality data for further analysis. In general, for the traditional microarray analysis, most established normalization methods are based on two assumptions: (1) the total number of target genes is large enough (>10,000); and (2) the expression level of the majority of genes is kept constant. However, microRNA (miRNA) arrays are usually spotted in low density, due to the fact that the total number of miRNAs is less than 2,000 and the majority of miRNAs are weakly or not expressed. As a result, normalization methods based on the above two assumptions are not applicable to miRNA profiling studies. In this review, we discuss a few representative microarray platforms on the market for miRNA profiling and compare the traditional methods with a few novel strategies specific for miRNA microarrays.
Keywords: microRNA; microarray; normalization; measurement error; qRT-PCR; bead array

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MDPI and ACS Style

Wang, B.; Xi, Y. Challenges for MicroRNA Microarray Data Analysis. Microarrays 2013, 2, 34-50.

AMA Style

Wang B, Xi Y. Challenges for MicroRNA Microarray Data Analysis. Microarrays. 2013; 2(2):34-50.

Chicago/Turabian Style

Wang, Bin; Xi, Yaguang. 2013. "Challenges for MicroRNA Microarray Data Analysis." Microarrays 2, no. 2: 34-50.

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