The process of producing microarray data involves multiple steps, some of which …

• Abstract
• Introduction
• Methods
• Results and Discussion
• Conclusion
• Acknowledgements
• References
• Figures

Biology Articles » Bioinformatics » MDQC: a new quality assessment method for microarrays based on quality control reports » Figures

Figures

- MDQC: a new quality assessment method for microarrays based on quality control reports

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Fig. 1. Quality measures for the acute lymphoblastic leukemia study: univariate analysis. Arrays that do not pass recommended thresholds are identified using solid points. The x axis contains the index of each array and the y axis shows the different quality measures contained in the report in their original scale.

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Fig. 2. Results of MDQC based on all measures of the QC report. The MDs (y axis) are computed using the robust S-estimator for each array (x axis). The solid, dashed and dotted lines correspond to the square root of the 90th, 95th and 99th percentile of the chi-squared distribution, respectively. Outlying arrays are identified using solid points.

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Fig. 3. Results of MDQC using the a priori grouping method. The MDs (y axis) within each group are computed using the robust S-estimator for each array (x axis). The scale of the y axis varies from one case to another. The solid, dashed and dotted lines correspond to the square root of the 90th, 95th and 99th percentile of the chi-squared distribution, respectively. Outlying arrays are identified using solid points.

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Fig. 4. Results of MDQC using the global PCA method. The MDs (y axis) are computed on the first four principal components for each array (x axis). The solid, dashed and dotted lines indicate the square root of the 90th, 95th and 99th percentile of the chi-squared distribution, respectively. Outlying arrays are identified using solid points.

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Fig. 5. Results of MDQC using a priori groups for the transplantation study. The scale of the y axis varies from one case to another. The solid, dashed and dotted lines correspond to the square root of the 90th, 95th and 99th percentile of the chi-squared distribution, respectively. Arrays that were re-run are identified with solid points, the results of the re-runs with solid triangles, and those arrays that could not be re-run (not enough RNA) with open triangles.

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Fig. 6. NUSE plots for 12 low-quality and 10 good-quality arrays.

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