Transformations for cDNA Microarray Data
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Xiangqin Cui
Two channel microarray data often contain systematic variations that can be minimized by data transformation prior to further analysis. The most commonly observed effects are revealed by viewing scatter plots of the logarithm of the ratio by the average logarithmic intensity of the two color channels (RI plots). In this paper we present a general model for signal intensity data with multiple error sources. We demonstrate how these sources of error influence the shape of an RI plot. We then compare some currently available transformation strategies in terms of their mechanism and performance on both simulated and real microarray data. A linlog transformation is proposed to stabilize the variance of the log ratios. We also propose a regional smoothing method to remove variation in log ratios due to spatial heterogeneity on the microarray surface. The discussed transformations represent an important initial step in microarray data analysis for both ratio-based and ANOVA methods.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Parameter estimation for the calibration and variance stabilization of microarray data
- Transformations for cDNA Microarray Data
- Supervised Detection of Regulatory Motifs in DNA Sequences
- Visualisation of Gene Expression Data - the GE-biplot, the Chip-plot and the Gene-plot
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Articles in the same Issue
- Article
- Use of Mixture Models in a Microarray-Based Screening Procedure for Detecting Differentially Represented Yeast Mutants
- Sampling Correction in Pedigree Analysis
- Parameter estimation for the calibration and variance stabilization of microarray data
- Transformations for cDNA Microarray Data
- Supervised Detection of Regulatory Motifs in DNA Sequences
- Visualisation of Gene Expression Data - the GE-biplot, the Chip-plot and the Gene-plot
- On the Power of Profiles for Transcription Factor Binding Site Detection
- An Empirical Bayesian Method for Differential Expression Studies Using One-Channel Microarray Data
