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. 2010 May 27;6(5):e1000786.
doi: 10.1371/journal.pcbi.1000786.

Making informed choices about microarray data analysis

Mark Reimers  1
Affiliations

Making informed choices about microarray data analysis

Mark Reimers. PLoS Comput Biol. .
No abstract available

PubMed Disclaimer

Conflict of interest statement

The author has declared that no competing interests exist.

Figures

Figure 1
Figure 1. Steps in a typical microarray analysis.
Figure 2
Figure 2. Plot of log ratio of intensity of an Affymetrix array.
This plot represents deviations of measures from chip GSM25526 (from GSE2552 in GEO) relative to the average across all chips (on the vertical axis) plotted against that average (in log2 units on the horizontal axis) as a technical variable. The black line indicates no trend; a loess fit to the trend is plotted in yellow. Clearly, the deviation of the trend from 0 is bigger than the standard deviation of the variation around the trend, a sign of a significant technical artifact.
Figure 3
Figure 3. False-color images of an Agilent array (AG1-Lab2-C1 from the MicroArray Quality Control project).
Green pixels represent probes whose value on this array is close to their average values across all samples. Red pixels represent probes whose values are more than 1.41 (square root of 2) times their average values across all samples, while dark blue pixels represent probes whose values are less than 0.71 (reciprocal of square root of 2) times those averages.
Figure 4
Figure 4. False-color image of an Illumina array.
Colors represent how the signal from a particular bead deviates from the average signal from that bead type across the array. The color scale is as for Figure 3.
See this image and copyright information in PMC

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