Publications (2)4.15 Total impact
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Article: Iterative linear regression by sector: renormalization of cDNA microarray data and cluster analysis weighted by cross homology.
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ABSTRACT: Two-color DNA microarray data has proven valuable in high-throughput expression profiling. However microarray expression ratios (log 2 ratios) are subject to measurement error from multiple causes. Transcript abundance is expected to be a linear function of signal intensity (y = x) where the typical gene is nonresponsive. Once linearity is confirmed, applying the model by fitting log-scale data with simple linear regression reduces the standard deviation of the log 2 ratios. After which fewer genes are selected by filtering methods. Comparing the residuals of regression to leverage measures can identify the best candidate genes. Spatial bias in log 2 ratio, defined by printing pin and detected by ANOVA, can be another source of measurement error. Independently applying the linear normalization method to the data from each pin can easily eliminate this error. Less easily addressed is the problem of cross-homology which is expected to correlate to cross-hybridization. Pair-wise comparison of genes demonstrate that genes with similar sequences are measured as having similar expression. While this bias cannot be easily eliminated, the effect this probable cross-hybridization can be minimized in clustering by weighting methods introduced here.03/2003; -
Article: Microarray data quality analysis: lessons from the AFGC project. Arabidopsis Functional Genomics Consortium.
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ABSTRACT: Genome-wide expression profiling with DNA microarrays has and will provide a great deal of data to the plant scientific community. However, reliability concerns have required the development data quality tests for common systematic biases. Fortunately, most large-scale systematic biases are detectable and some are correctable by normalization. Technical replication experiments and statistical surveys indicate that these biases vary widely in severity and appearance. As a result, no single normalization or correction method currently available is able to address all the issues. However, careful sequence selection, array design, experimental design and experimental annotation can substantially improve the quality and biological of microarray data. In this review, we discuss these issues with reference to examples from the Arabidopsis Functional Genomics Consortium (AFGC) microarray project.Plant Molecular Biology 02/2002; 48(1-2):119-31. · 4.15 Impact Factor
