Statistical Applications in Genetics and Molecular Biology - newarticles (fwd)

["Clarice G.B. Demetrio" <clarice@carpa.ciagri.usp.br>]

---------- Forwarded message ----------
Date: Thu, 8 Apr 2004 12:39:36 -0700 (PDT)
From: Nicholas P. Jewell <mm-9196-398660@bepress.com>
To: clarice@carpa.ciagri.usp.br
Subject: Statistical Applications in Genetics and Molecular Biology - new
    articles


The Berkeley Electronic Press, together with editors
Nicholas P. Jewell, University of California, Berkeley,
Gary Churchill, The Jackson Laboratory, and Elizabeth
Thompson, University of Washington, is pleased to announce
the publication of several new articles in its
peer-reviewed electronic journal, STATISTICAL APPLICATIONS
IN GENETICS AND MOLECULAR BIOLOGY (SAGMB). A call for
papers follows below. Among the newly published articles
are the following (click links to access full text;
abstracts may be found at the conclusion of this
message):

Katy L. Simonsen and Lauren M. McIntyre "Using Alpha
Wisely: Improving Power to Detect Multiple QTL".
http://www.bepress.com/sagmb/vol3/iss1/art1

Mark R. Segal, Jason D. Barbour, and Robert M. Grant
"Relating HIV-1 Sequence Variation to Replication Capacity
via Trees and Forests".
http://www.bepress.com/sagmb/vol3/iss1/art2

Gordon K. Smyth "Linear Models and Empirical Bayes Methods
for Assessing Differential Expression in Microarray
Experiments".
http://www.bepress.com/sagmb/vol3/iss1/art3

Mark J. van der Laan, Sandrine Dudoit, and Sunduz Keles
"Asymptotic Optimality of Likelihood-Based
Cross-Validation".
http://www.bepress.com/sagmb/vol3/iss1/art4



Statistical Applications in Genetics and Molecular Biology
(SAGMB) publishes significant research on the application
of statistical ideas to problems arising from computational
biology. The focus of the papers should be on the relevant
statistical issues but should contain a succinct
description of the relevant biological problem being
considered. The range of topics is wide and will include
topics such as linkage mapping, association studies, gene
finding and sequence alignment, protein structure
prediction, design and analysis of microarray data,
molecular evolution and phylogenetic trees, DNA topology,
and data base search strategies. Both original research and
review articles will be warmly received. Among the SAGMB
advantages:

- editorial decision guaranteed within 10 weeks

- fewer rounds of revision

- immediate publication upon acceptance

- distribution to a network of 20,000 researchers

- inclusion of supplementary materials such as data sets
and video files

We invite you to submit your next paper to Statistical
Applications in Genetics and Molecular Biology. To do so,
simply visit http://www.bepress.com/sagmb and click the
"Submit Article" link in the right corner.


EDITORIAL BOARD MEMBERS & AFFILIATIONS

Rebecca Doerge, Purdue University
Sandrine Dudoit, University of California, Berkeley
Mark van der Laan, University of California, Berkeley
Laura Lazzeroni, Stanford University
Shili Lin, Ohio State University
Jun Liu, Harvard University
Mary Sara McPeek, University of Chicago
Bernard Prum, CNRS-INRA-Université d'Evry
Andrey Rzhetsky, Columbia University
Terry Speed, University of California, Berkeley
Rob Tibshirani, Stanford University
Hongyu Zhao, Yale University


____________________
CITATIONS AND ABSTRACTS OF RECENTLY PUBLISHED PAPERS



Katy L. Simonsen and Lauren M. McIntyre (2004) "Using Alpha
Wisely: Improving Power to Detect Multiple QTL",
Statistical Applications in Genetics and Molecular Biology:
Vol. 3: No. 1, article 1.

http://www.bepress.com/sagmb/vol3/iss1/art1

ABSTRACT:
The increase in the number of available markers for many
experimental populations has led to QTL studies with ever
increasing marker numbers and densities. The resulting
conundrum is that as marker density increases, so does the
multiple testing problem. It is important to re-examine the
detection of multiple QTL in light of increasing marker
density. We explore through simulation whether existing
methods have achieved the maximum possible power for
detecting multiple QTL and whether increasing the marker
density is an effective strategy for locating multiple QTL.
In addition to existing methods, such as the maximum, the
CET, and the Benjamini-Hochberg and Benjamini-Yekutieli
procedures, we propose and evaluate the complete set of
order statistics with their corresponding empirical joint
distribution. We examine these statistics in conjunction
with a novel application of the alpha-spending approach,
providing a less conservative solution to the problem of
controlling the false discovery rate (FDR) in multiple
tests. We conducted a simulation study to assess the
relative power of these approaches as well as their ability
to control FDR. We find that several of the new approaches
have a reasonable FDR, and can substantially improve the
experimenter's ability to detect multiple QTL compared to
existing approaches in many cases; however, the
Benjamini-Hochberg procedure remains a very reasonable
choice. The methods are applied to a nine-trait Oat
vernalization dataset.


Mark R. Segal, Jason D. Barbour, and Robert M. Grant (2004)
"Relating HIV-1 Sequence Variation to Replication Capacity
via Trees and Forests", Statistical Applications in
Genetics and Molecular Biology: Vol. 3: No. 1, article 2.

http://www.bepress.com/sagmb/vol3/iss1/art2

ABSTRACT:
The problem of relating genotype (as represented by amino
acid sequence) to phenotypes is distinguished from standard
regression problems by the nature of sequence data. Here we
investigate an instance of such a problem where the
phenotype of interest is HIV-1 replication capacity and
contiguous segments of protease and reverse transcriptase
sequence constitutes genotype. A variety of data analytic
methods have been proposed in this context. Shortcomings of
select techniques are contrasted with the advantages
afforded by tree-structured methods. However,
tree-structured methods, in turn, have been criticized on
grounds of only enjoying modest predictive performance. A
number of ensemble approaches (bagging, boosting, random
forests) have recently emerged, devised to overcome this
deficiency. We evaluate random forests as applied in this
setting, and detail why prediction gains obtained in other
situations are not realized. Other approaches including
logic regression, support vector machines and neural
networks are also applied. We interpret results in terms of
HIV-1 reverse transcriptase structure and function.


Gordon K. Smyth (2004) "Linear Models and Empirical Bayes
Methods for Assessing Differential Expression in Microarray
Experiments", Statistical Applications in Genetics and
Molecular Biology: Vol. 3: No. 1, article 3.

http://www.bepress.com/sagmb/vol3/iss1/art3

ABSTRACT:
The problem of identifying differentially expressed genes
in designed microarray experiments is considered. Lonnstedt
and Speed (2002) derived an expression for the posterior
odds of differential expression in a replicated two-color
experiment using a simple hierarchical parametric model.
The purpose of this paper is to develop the hierarchical
model of Lonnstedt and Speed (2002) into a practical
approach for general microarray experiments with arbitrary
numbers of treatments and RNA samples. The model is reset
in the context of general linear models with arbitrary
coefficients and contrasts of interest. The approach
applies equally well to both single channel and two color
microarray experiments. Consistent, closed form estimators
are derived for the hyperparameters in the model. The
estimators proposed have robust behavior even for small
numbers of arrays and allow for incomplete data arising
from spot filtering or spot quality weights. The posterior
odds statistic is reformulated in terms of a moderated
t-statistic in which posterior residual standard deviations
are used in place of ordinary standard deviations. The
empirical Bayes approach is equivalent to shrinkage of the
estimated sample variances towards a pooled estimate,
resulting in far more stable inference when the number of
arrays is small. The use of moderated t-statistics has the
advantage over the posterior odds that the number of
hyperparameters which need to estimated is reduced; in
particular, knowledge of the non-null prior for the fold
changes are not required. The moderated t-statistic is
shown to follow a t-distribution with augmented degrees of
freedom. The moderated t inferential approach extends to
accommodate tests of composite null hypotheses through the
use of moderated F-statistics. The performance of the
methods is demonstrated in a simulation study. Results are
presented for two publicly available data sets.


Mark J. van der Laan, Sandrine Dudoit, and Sunduz Keles
(2004) "Asymptotic Optimality of Likelihood-Based
Cross-Validation", Statistical Applications in Genetics and
Molecular Biology: Vol. 3: No. 1, article 4.

http://www.bepress.com/sagmb/vol3/iss1/art4

ABSTRACT:
Likelihood-based cross-validation is a statistical tool for
selecting a density estimate based on n i.i.d. observations
from the true density among a collection of candidate
density estimators. General examples are the selection of a
model indexing a maximum likelihood estimator, and the
selection of a bandwidth indexing a nonparametric (e.g.
kernel) density estimator. In this article, we establish a
finite sample result for a general class of
likelihood-based cross-validation procedures (as indexed by
the type of sample splitting used, e.g. V-fold
cross-validation). This result implies that the
cross-validation selector performs asymptotically as well
(w.r.t. to the Kullback-Leibler distance to the true
density) as a benchmark model selector which is optimal for
each given dataset and depends on the true density. Crucial
conditions of our theorem are that the size of the
validation sample converges to infinity, which excludes
leave-one-out cross-validation, and that the candidate
density estimates are bounded away from zero and infinity.
We illustrate these asymptotic results and the practical
performance of likelihood-based cross-validation for the
purpose of bandwidth selection with a simulation study.
Moreover, we use likelihood-based cross-validation in the
context of regulatory motif detection in DNA sequences.



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