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Comparing transformation methods for DNA microarray data. | LitMetric

Comparing transformation methods for DNA microarray data.

BMC Bioinformatics

Clinical Epidemiology and Biostatistics, Academisch Medisch Centrum, University of Amsterdam, 1100 DD, Netherlands.

Published: June 2004

AI Article Synopsis

  • DNA microarray data requires multiple normalization steps for accurate analysis, such as background subtraction and smoothing, but the best methods can vary by study.
  • Researchers evaluated transformation methods using a specific statistical measure, finding that the effectiveness of these methods depends on the actual data being analyzed.
  • Incorporating replicates in experiments and adjusting for variance behavior is crucial for selecting the right transformation approach to ensure reliable results.

Article Abstract

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 F-like 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 parameters for the transformation methods depends on the data. Further, the behavior of the variance ratio, under the null hypothesis of zero biological variance, appears to depend on the choice of parameters.

Conclusions: The use of replicates in microarray experiments is important. Adjustment for the null-hypothesis behavior of the variance ratio is critical to the selection of transformation method.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC449698PMC
http://dx.doi.org/10.1186/1471-2105-5-77DOI Listing

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