Affymetrix has designed new algorithms for monitoring GeneChip® expression data. These statistical algorithms, created in response to customer input, were designed to accommodate the typical distribution of data found in microarray experiments. The new statistical algorithms employ standard statistical techniques and are optimized to accommodate advancements in array and probe selection technology. They provide accurate, high-quality analysis for GeneChip® array data. This new, statistically based approach provides:
– Calculation of statistical significance for detection and change calls (p-values) and confidence limits for log ratio values (fold change).
– Easily tunable parameters that enable the user to vary the stringency of the analyses.
– Elimination of negative expression values observed with the empirical algorithms.
– Easily referenced standard statistical techniques.
This technical note reviews the design and testing for Affymetrix® new statistical algorithms and explores performance characteristics of the statistical algorithms versus the previous empirical algorithms.
Experimental Design: How the New Algorithms Were Selected and Optimized
To select components for the new algorithms and test for optimization, a “training” data set was required. To conduct this comprehensive testing, each transcript group was spiked into a labeled mixture of RNA from a tissue source in an experimental design known as a Latin Square. A Latin Square is used to accurately monitor the detectability of transcripts over a range of concentrations. It also allows the statistical analysis of patterns and variability in repeated measurements in a systematic fashion, thus revealing patterns in the data and allowing rigorous comparisons. The Latin