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On the Analysis of Optical Mapping Data - University of Wisconsin ...

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36<br />

0.20<br />

0.15<br />

0.10<br />

0.05<br />

0.00<br />

0.700 − 0.005<br />

10 20 30 40 50 60<br />

0.750 − 0.005 0.800 − 0.005<br />

0.700 − 0.003 0.750 − 0.003<br />

0.800 − 0.003<br />

0.20<br />

P(X = x)<br />

0.15<br />

0.10<br />

0.05<br />

0.00<br />

0.20<br />

0.15<br />

0.10<br />

0.05<br />

0.00<br />

0.700 − 0.001 0.750 − 0.001<br />

0.800 − 0.001<br />

10 20 30 40 50 60<br />

x<br />

10 20 30 40 50 60<br />

Figure 2.8 A hanging rootogram comparing <strong>the</strong> observed distribution <strong>of</strong> <strong>the</strong> number <strong>of</strong><br />

fragments in GM07535 optical maps to various simulated map sets. The rootogram, an<br />

innovation due to John Tukey, is intended to compare <strong>the</strong> distribution <strong>of</strong> a discrete random<br />

variable to a reference distribution. Here, <strong>the</strong> continuous reference curve represents <strong>the</strong><br />

relative frequencies <strong>of</strong> number <strong>of</strong> fragments observed in <strong>the</strong> GM07535 data and is <strong>the</strong> same<br />

in each panel. The vertical lines represent corresponding frequencies in simulated data, but<br />

<strong>the</strong>y ‘hang’ from <strong>the</strong> reference ra<strong>the</strong>r than starting from <strong>the</strong> origin. Systematic departures<br />

from <strong>the</strong> reference are indicated by patterns <strong>of</strong> <strong>the</strong> lower endpoints relative to <strong>the</strong> origin.<br />

Also, <strong>the</strong> vertical axis plots <strong>the</strong> square root <strong>of</strong> <strong>the</strong> proportions (hence <strong>the</strong> name rootogram)<br />

to emphasize smaller probabilities.

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