modelDropout_gui: Model And Plot Drop-out Events
Description
Model the probability of drop-out and plot graphs.Usage
modelDropout_gui(env = parent.frame(), savegui = NULL, debug = FALSE,
parent = NULL)
Arguments
env
environment in wich to search for data frames and save result.
savegui
logical indicating if GUI settings should be saved in the environment.
debug
logical indicating printing debug information.
parent
widget to get focus when finished.
Details
calculateDropout score drop-out events relative to a user
defined LDT in four different ways:
(1) by reference to the low molecular weight allele (Method1),
(2) by reference to the high molecular weight allele (Method2),
(3) by reference to a random allele (MethodX), and
(4) by reference to the locus (MethodL).
Options 1-3 are recommended by the DNA commision (see reference),
while option 4 is included for experimental purposes.
Options 1-3 may discard many dropout events while option 4 catches all
drop-out events. On the other hand options 1-3 can score events below
the LDT, while option 4 cannot, making accurate predictions possible
below the LDT.
Method X/1/2 records the peak height of the partner allele to be used as
the explanatory variable in the logistic regression. The locus method L also
do this when there has been a drop-out, if not the the mean peak height for
the locus is used. Peak heights for the locus method are stored in a
separate column.
Using the scored drop-out events and the peak heights of the surviving
alleles the probability of drop-out can be modelled by logistic regression
as described in Appendix B the first reference.
P(dropout|H) = B0 + B1*H, where 'H' is the peak height or log(peak height).
This produces a plot with the predicted probabilities for a range of peak heights.
There are options to print the model parameters, mark the stochastic
threshold at a specified probability of drop-out, include the underlying
observations, and to calculate a specified prediction interval.
A conservative estimate of the stochastic threshold can be calculated
from the prediction interval: the risk of observing a drop-out probability
greater than the specified threshold limit, at the conservative peak height,
is less than a specified value (e.g. 1-0.95=0.05). By default the gender
marker is excluded from the dataset used for modelling, and the peak height
is used as explanatory variable. The average peak height 'H' can be used
instead of the allele/locus peak height. Optionally, the logarithm of the peak
height can be used. To evaluate the goodness of fit for the logistic
regression the Hosmer-Lemeshow test is used. A value below 0.05 indicates
a poor fit. Alternatives to the logistic regression method are discussed in
reference 4 and 5. [13,32].
Explanation of the result:
Dropout - all alleles are scored according to the limit of detection threshold (LDT).
This is the observations and is not used for modelling.
Rfu - peak height of the surviving allele.
MethodX - a random reference allele is selected and drop-out is scored in
relation to the the partner allele.
Method1 - the low molecular weight allele is selected and drop-out is
scored if the high molecular weight allele is missing.
Method2 - the high molecular weight allele is selected and drop-out is
scored if the low molecular weight allele is missing.
MethodL - drop-out is scored per locus i.e. drop-out if any allele is missing.
MethodL.Ph - peak height of the surviving allele if one allele has droped out,
or the average peak height if no drop-out.References
Peter Gill et.al.,
DNA commission of the International Society of Forensic Genetics:
Recommendations on the evaluation of STR typing results that may
include drop-out and/or drop-in using probabilistic methods,
Forensic Science International: Genetics, Volume 6, Issue 6, December 2012,
Pages 679-688, ISSN 1872-4973, 10.1016/j.fsigen.2012.06.002.
http://www.sciencedirect.com/science/article/pii/S1872497312001354
Peter Gill, Roberto Puch-Solis, James Curran,
The low-template-DNA (stochastic) threshold-Its determination relative to
risk analysis for national DNA databases,
Forensic Science International: Genetics, Volume 3, Issue 2, March 2009,
Pages 104-111, ISSN 1872-4973, 10.1016/j.fsigen.2008.11.009.
http://www.sciencedirect.com/science/article/pii/S1872497308001798
H. DW Jr., S. Lemeshow, Applied Logistic Regression, John Wiley & Sons, 2004.
A.A. Westen, L.J.W. Grol, J. Harteveld, A.S. Matai, P. de Knijff, T. Sijen,
Assessment of the stochastic threshold, back- and forward stutter filters
and low template techniques for NGM,
Forensic Science International: Genetetics, Volume 6, Issue 6 December 2012,
Pages 708-715, ISSN 1872-4973, 10.1016/j.fsigen.2012.05.001.
http://www.sciencedirect.com/science/article/pii/S1872497312001172
R. Puch-Solis, A.J. Kirkham, P. Gill, J. Read, S. Watson, D. Drew,
Practical determination of the low template DNA threshold,
Forensic Science International: Genetetics, Volume 5, Issue 5, November 2011,
Pages 422-427, ISSN 1872-4973, 10.1016/j.fsigen.2010.09.001.
http://www.sciencedirect.com/science/article/pii/S1872497310001511