Statistical Analysis of Low-level High Density Oligonucleotide Array Data

Benjamin M. Bolstad

University of California, Berkeley
Berkeley CA
USA

bolstad@stat.berkeley.edu



When running an experiment using the Affymetrix GeneChip(R) system, it is important to have good 
quality gene expression estimates. At the low level, we work with Perfect Match (PM) probe intensity 
data. A GeneChip(R) has multiple PM probes, each interrogating the same gene. Information from these 
probes may be combined together to compute an expression estimate.


I will discuss the three major steps used when computing the Robust Multi-chip Average (RMA) 
expression measure: background correction, normalization and summarization.


Background correction is the process of removing noise, particularly in the low intensity 
range. The goal of normalization is to remove unwanted non-biological sources of variation. 
To normalize data from multiple arrays, RMA makes use of a simple non-parameteric, non-linear
algorithm called quantile normalization. Summarization is the process where information from 
multiple probes is combined to compute an expression measure. In the context of RMA, this is 
done by fitting a robust form of linear model to probe information from multiple chips.


I will then compare RMA with other established expression measures using two very important 
statistical measures: bias and variance.