On the Analysis of Optical Mapping Data - University of Wisconsin ...
On the Analysis of Optical Mapping Data - University of Wisconsin ...
On the Analysis of Optical Mapping Data - University of Wisconsin ...
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35<br />
30<br />
0.700 − 0.005<br />
0 50 100 150 200 250<br />
0.750 − 0.005 0.800 − 0.005<br />
20<br />
10<br />
0<br />
−10<br />
0.700 − 0.003 0.750 − 0.003<br />
0.800 − 0.003<br />
30<br />
20<br />
difference<br />
10<br />
0<br />
30<br />
0.700 − 0.001 0.750 − 0.001<br />
0.800 − 0.001<br />
−10<br />
20<br />
10<br />
0<br />
−10<br />
0 50 100 150 200 250<br />
mean<br />
0 50 100 150 200 250<br />
Figure 2.7 As we saw in Figure 2.2, it is <strong>of</strong>ten helpful to look at rotated Q-Q plots so that<br />
deviations from <strong>the</strong> diagonal are emphasized. In this mean-difference plot, which effectively<br />
rotates each panel in Figure 2.6 clockwise by 45 ◦ , systematic patterns are apparent that were<br />
not obvious in <strong>the</strong> earlier plot. In particular, this plot gives more insight into <strong>the</strong> subtler<br />
effect <strong>of</strong> <strong>the</strong> spurious cut rate. Recall that <strong>the</strong> distribution <strong>of</strong> fragment lengths is roughly<br />
comparable to an exponential distribution with mean 20 Kb, so more than 99% <strong>of</strong> fragments<br />
are shorter than 100 Kb.