Quality determination and the repair of poor quality spots in array experiments

Background: A common feature of microarray experiments is the occurence of missing gene expression data. These missing values occur for a variety of reasons, in particular, because of the filtering of poor quality spots and the removal of undefined values when a logarithmic transformation is applied to negative background-corrected intensities. The efficiency and power of an analysis performed can be substantially reduced by having an incomplete matrix of gene intensities. Additionally, most statistical methods require a complete intensity matrix. Furthermore, biases may be introduced into analyses through missing information on some genes. Thus methods for appropriately replacing (imputing) missing data and/or weighting poor quality spots are required. Results: We present a likelihood-based method for imputing missing data or weighting poor quality spots that requires a number of biological or technical replicates. This likelihood-based approach assumes that the data for a given spot...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Brian D. M. Tom, Walter R. Gilks, Elizabeth T. Brooke-Powell, James W. Ajioka
• Keywords: BMCBI 2005, Gene Expression, Mixture Distributions, Posterior Probabilities