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Exploratory differential gene expression analysis in microarray experiments with no or limited replication.

Loguinov AV, Mian IS, Vulpe CD - Genome Biol. (2004)

Bottom Line: We describe an exploratory, data-oriented approach for identifying candidates for differential gene expression in cDNA microarray experiments in terms of alpha-outliers and outlier regions, using simultaneous tolerance intervals relative to the line of equivalence (Cy5 = Cy3).We demonstrate the improved performance of our approach over existing single-slide methods using public datasets and simulation studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Nutritional Sciences and Toxicology, University of California at Berkeley, Morgan Hall, Berkeley, CA 94720, USA. Avl53@aol.com

ABSTRACT
We describe an exploratory, data-oriented approach for identifying candidates for differential gene expression in cDNA microarray experiments in terms of alpha-outliers and outlier regions, using simultaneous tolerance intervals relative to the line of equivalence (Cy5 = Cy3). We demonstrate the improved performance of our approach over existing single-slide methods using public datasets and simulation studies.

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Related in: MedlinePlus

QQNP with simulation envelopes (based on 1,000 random samples from a normal population) for residuals of nine datasets from Hughes et al. [18]. Channel intensity values were log(base 2)-transformed and normalized. The envelopes are depicted as dashed red lines.
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Figure 8: QQNP with simulation envelopes (based on 1,000 random samples from a normal population) for residuals of nine datasets from Hughes et al. [18]. Channel intensity values were log(base 2)-transformed and normalized. The envelopes are depicted as dashed red lines.

Mentions: The other nine datasets show a similar pattern (Figures 2,3,4,5,6,7,8,9,10). Figure 2 shows nine scatter plots with tolerance ellipses for the empirical log-transformed normalized channel intensities. There are strong bivariate outliers and differential gene expression candidates will be represented by Y-outliers. A data point which is an X-outlier or Y-X-outlier probably represents a technical gross error. Figure 3 represents scatter plots for simulated data produced using robust estimates of location and scale parameters for the corresponding empirical datasets. A 99.99% tolerance ellipse covers the simulated data points with no outliers. Figure 4 displays results after outlier removal from the empirical data using a simple cut-off and ignoring heteroscedasticity, if any. The majority of data points look like regular observations sampled from a bivariate normal population.


Exploratory differential gene expression analysis in microarray experiments with no or limited replication.

Loguinov AV, Mian IS, Vulpe CD - Genome Biol. (2004)

QQNP with simulation envelopes (based on 1,000 random samples from a normal population) for residuals of nine datasets from Hughes et al. [18]. Channel intensity values were log(base 2)-transformed and normalized. The envelopes are depicted as dashed red lines.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC395768&req=5

Figure 8: QQNP with simulation envelopes (based on 1,000 random samples from a normal population) for residuals of nine datasets from Hughes et al. [18]. Channel intensity values were log(base 2)-transformed and normalized. The envelopes are depicted as dashed red lines.
Mentions: The other nine datasets show a similar pattern (Figures 2,3,4,5,6,7,8,9,10). Figure 2 shows nine scatter plots with tolerance ellipses for the empirical log-transformed normalized channel intensities. There are strong bivariate outliers and differential gene expression candidates will be represented by Y-outliers. A data point which is an X-outlier or Y-X-outlier probably represents a technical gross error. Figure 3 represents scatter plots for simulated data produced using robust estimates of location and scale parameters for the corresponding empirical datasets. A 99.99% tolerance ellipse covers the simulated data points with no outliers. Figure 4 displays results after outlier removal from the empirical data using a simple cut-off and ignoring heteroscedasticity, if any. The majority of data points look like regular observations sampled from a bivariate normal population.

Bottom Line: We describe an exploratory, data-oriented approach for identifying candidates for differential gene expression in cDNA microarray experiments in terms of alpha-outliers and outlier regions, using simultaneous tolerance intervals relative to the line of equivalence (Cy5 = Cy3).We demonstrate the improved performance of our approach over existing single-slide methods using public datasets and simulation studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Nutritional Sciences and Toxicology, University of California at Berkeley, Morgan Hall, Berkeley, CA 94720, USA. Avl53@aol.com

ABSTRACT
We describe an exploratory, data-oriented approach for identifying candidates for differential gene expression in cDNA microarray experiments in terms of alpha-outliers and outlier regions, using simultaneous tolerance intervals relative to the line of equivalence (Cy5 = Cy3). We demonstrate the improved performance of our approach over existing single-slide methods using public datasets and simulation studies.

Show MeSH
Related in: MedlinePlus