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

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Actual alignments
Predicted by model
Density
0.015
0.010
0.005
0.000
0.015
0.010
0.005
0.000
75 80 85 90 95 100 105
50 55 60 65 70 75
20 25 30 35 40 45 50
Location (Mb)
0.015
0.010
0.005
0.000
Figure 3.11 Estimated thinning rates. The data are approximately 10,000 simulated maps
from human chromosome 14. The first curve is the kernel density estimate of locations
obtained from alignments declared significant. The second curve is the density of the true
locations of the same simulated maps, but with weights given by model (3.2).
The fitted model was then used to estimate P ( aligned | M ) for a new set of maps simulated
from chromosome 14, which were actually aligned as well. Figure 3.11 compares the kernel
density estimate obtained from aligned locations with the estimated density of the true
locations of all simulated maps, but with weights given by model (3.2). The estimated
densities estimated by the two methods are very close, suggesting that we can do away with
the alignment step without substantial drawbacks.
The calibration provided by (3.2) can also help in preliminary filtering of optical maps.
Currently, it is common to entirely remove maps shorter than a certain length (typically 300
Kb) from analysis as they are expected to have little information. Our observations would
suggest that ψ(M) is a better quantity on which to base this decision. This is also related to
our earlier discussion motivated by a comparison of Figures 3.8 and 3.9. The subset of maps
that have a high probability of being aligned based on ψ(M) but are not actually aligned to
the reference are likely to contain a higher proportion of maps that originate from regions of
the genome not represented in the reference copy.
3.4.3 Other topics
Choice of Null hypothesis: Independence of M and ˜G is not necessarily the obvious
hypothesis to test when determining significance. It is not unlikely for an optical map,