Abstract
BackgroundA 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.ResultsWe 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 arising from each channel of a two-dye (two-channel) cDNA microarray comparison experiment independently come from a three-component mixture distribution – the parameters of which are estimated through use of a constrained E-M algorithm. Posterior probabilities of belonging to each component of the mixture distributions are calculated and used to decide whether imputation is required. These posterior probabilities may also be used to construct quality weights that can down-weight poor quality spots in any analysis performed afterwards. The approach is illustrated using data obtained from an experiment to observe gene expression changes with 24 hr paclitaxel (Taxol ®) treatment on a human cervical cancer derived cell line (HeLa).ConclusionAs the quality of microarray experiments affect downstream processes, it is important to have a reliable and automatic method of identifying poor quality spots and arrays. We propose a method of identifying poor quality spots, and suggest a method of repairing the arrays by either imputation or assigning quality weights to the spots. This repaired data set would be less biased and can be analysed using any of the appropriate statistical methods found in the microarray literature.
Highlights
A common feature of microarray experiments is the occurence of missing gene expression data
The importance of experimental design and quality control cannot be over-emphasised, as experiments that have not been designed based on sound principles are more likely to produce poor quality data, which in turn affects all downstream processes and lead to unreliable or misleading results
Example We apply our method to background uncorrected intensity data obtained from an experiment performed to observe gene expression changes with 24 hr paclitaxel (Taxol ®) treatment on a human cervical cancer derived cell line (Hela)
Summary
A common feature of microarray experiments is the occurence of missing gene expression data. The importance of experimental design and quality control cannot be over-emphasised, as experiments that have not been designed based on sound principles are more likely to produce poor quality data, which in turn affects all downstream processes (image analysis, transformation, normalization, statistical analysis) and lead to unreliable or misleading results. Work by researchers such as [1,2,3], etc. Far fewer researchers have tackled the issue of quality control, some exceptions being the work done by [4,5,6,7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
