Microarray Analysis Techniques - Aggregation and Normalization

Aggregation and Normalization

Comparing two different arrays, or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by local regression. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots. A common method for evaluating how well normalized an array is, is to plot an MA plot of the data.

Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA) is a normalization approach that does not take advantage of these mismatch spots, but still must summarize the perfect matches through median polish. The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed. Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.

The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.

Factor Analysis for Robust Microarray Summarization (FARMS) is a model-based technique for summarizing array data at perfect match probe level. It is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise. According to the Affycomp benchmark FARMS outperformed all other summarizations methods with respect to sensitivity and specificity.