Inferring the location and effect of tumor suppressor genes
by instability-selection modeling of allelic-loss data
Inferring the location and effect of tumor suppressor genes
by instability-selection modeling of allelic-loss data
Michael A. Newton , and Yoonjung Lee.
Biometrics , 56, 1088-1097, 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.
Allelic-loss 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 instability-selection model of allelic-loss data, including
likelihood-based parameter estimation and
hypothesis testing. By considering a special
complete-data case, we derive an approximate calibration method
for hypothesis tests of sporadic deletion. Parametric bootstrap
and Bayesian computations are also developed.
Data from three allelic-loss 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 allelic-loss data.
Statistics in Medicine 17 , 1425-1445.
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, log-likelihood 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 (S-plus 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 (S-plus function), code
for parameter estimation should be run on S-plus. The remaining code can
be run on either S-plus or R. To run code as a batch job on
S-plus, 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 --no-save -v 20 < s.mcmc > s.out &
Results of the calculation are stored in the file results .
- Bugs reported and fixed