title: Challenges for MicroRNA Microarray Data Analysis.
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.
authors.affiliation: Department of Mathematics and Statistics, University of South Alabama, 411 University BLVD N, Room 325, Mobile, AL 36688, USA; bwang@southalabama.edu.
authors.forename: Bin
authors.initials: B
authors.lastname: Wang
authors.forename: Yaguang
authors.initials: Y
authors.lastname: Xi
keywords: bead array; measurement error; microRNA; microarray; normalization; qRT-PCR