Inferring the location and effect of tumor suppressor genes
by instabilityselection modeling of allelicloss data
Inferring the location and effect of tumor suppressor genes
by instabilityselection modeling of allelicloss data
Michael A. Newton , and Yoonjung Lee.
Biometrics , 56, 10881097, 2000.
(Originally issued May 7, 1999, as Department of Biostatistics and
Medical Informatics Technical Report #135.)
Abstract:
Cancerous tumor growth creates cells with abnormal DNA.
Allelicloss experiments identify genomic deletions in cancer
cells, but sources of variation and intrinsic dependencies
complicate inference about the location and effect of suppressor
genes; such genes are the target of these experiments and
are thought to be involved in tumor development.
We investigate properties
of an instabilityselection model of allelicloss data, including
likelihoodbased parameter estimation and
hypothesis testing. By considering a special
completedata case, we derive an approximate calibration method
for hypothesis tests of sporadic deletion. Parametric bootstrap
and Bayesian computations are also developed.
Data from three allelicloss studies are reanalyzed to illustrate
the methods.
Key words: Allelic imbalance; Cancer gene mapping; Chromosomal
deletions; Correlated binary data; LOD score; Loss of heterozygosity.
This manuscript is a follow up to
M.A. Newton ,
M.N. Gould, C. A. Reznikoff, and J.D. Haag (1998).
On the statistical analysis of allelicloss data.
Statistics in Medicine 17 , 14251445.
Data Sets Analyzed (in R/Splus dput/dget format):
Allelic
loss data is in the matrix data (LOH=1,MOH=0,no data=1);
marker positions relative to (0,1) are in the vector pos .
R/Splus code
 Maximum likelihood estimation

Source for data input: Download one of the data files listed above.

Source code for likelihood evaluation : mloglik.null.s
(under the null ),
mloglik.alt.s
(under the alternative).

Source code for maximum likelihood estimation: mle.null.s
(under the null), mle.alt.s
(under the alternative).
Under the null hypothesis, loglikelihood function was maximized over
lambda, and delta. Under the alternative hypothesis, it was maximized
over lambda, delta, omega, and locus under the constraint that omega >=
delta. For maximization nlminb (Splus function) was used.

Source code for simple estimators :
simple.est.null.s
(under the null), simple.est.alt.s
(under the alternative).
See comments in the source files to see how simple estimators were
computed.

Source code for plotting profile lod curves: lod.plot.s

Brief instructions: In order to use nlminb (Splus function), code
for parameter estimation should be run on Splus. The remaining code can
be run on either Splus or R. To run code as a batch job on
Splus, type: Splus BATCH <source.s>
<output.out> &.
 Bayesian analysis via MCMC
 Source for data input: Download one of the data files
listed above.
 Source code for the missing data proposal,
s.q
 Main source file s.mcmc
 Brief instructions: The R/Splus code provided here was used to implement
the Bayesian computations described in the above technical report
TR135.
To use, download a data file from the above list, the
file s.q which performs one of the update steps, and the main file,
s.mcmc . Typical use will involve a few tests in which the mcmc
object in s.mcmc is set to force a short run. A production run will
use much longer settings and may take hours on a fast workstation.
I usually run this batch, with a command:
R nosave v 20 < s.mcmc > s.out &
Results of the calculation are stored in the file results .
 Bugs reported and fixed