Comparing transformation methods for DNA microarray data

Background: When DNA microarray data are used for gene clustering, genotype/phenotype correlation studies, or tissue classification the signal intensities are usually transformed and normalized in several steps in order to improve comparability and signal/noise ratio. These steps may include subtraction of an estimated background signal, subtracting the reference signal, smoothing (to account for nonlinear measurement effects), and more. Different authors use different approaches, and it is generally not clear to users which method they should prefer. Results: We used the ratio between biological variance and measurement variance (which is an Flike statistic) as a quality measure for transformation methods, and we demonstrate a method for maximizing that variance ratio on real data. We explore a number of transformations issues, including Box-Cox transformation, baseline shift, partial subtraction of the log-reference signal and smoothing. It appears that the optimal choice of paramet...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Helene H. Thygesen, Aeilko H. Zwinderman
• Keywords: BMCBI 2004, DNA Microarray Data, Variance, Variance Ratio