Statistical methods of background correction for Illumina BeadArray data.
BACKGROUND
Advances in technology have made different microarray platforms available. Among the many, Illumina BeadArrays are relatively new and have captured significant market share. With BeadArray technology, high data quality is generated from low sample input at reduced cost. However, the analysis methods for Illumina BeadArrays are far behind those for Affymetrix oligonucleotide arrays, and so need to be improved.


RESULTS
In this article, we consider the problem of background correction for BeadArray data. One distinct feature of BeadArrays is that for each array, the noise is controlled by over 1000 bead types conjugated with non-specific oligonucleotide sequences. We extend the robust multi-array analysis (RMA) background correction model to incorporate the information from negative control beads, and consider three commonly used approaches for parameter estimation, namely, non-parametric, maximum likelihood estimation (MLE) and Bayesian estimation. The proposed approaches, as well as the existing background correction methods, are compared through simulation studies and a data example. We find that the maximum likelihood and Bayes methods seem to be the most promising.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Xie
Yang
Y
Division of Biostatistics, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, USA. yang.xie@utsouthwestern.edu
Wang
Xinlei
X
Story
Michael
M
D019295:Computational Biology; D020869:Gene Expression Profiling / Q000379:methods*; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*