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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, October 26, 2004}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Jianqing Fan}
\centerline{\sl Department of Operation Research and Financial Engineering}
\centerline{\sl Princeton University}
\centerline{Princeton, NJ 08544}
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\centerline{\bf Normalization and significant analysis of microarrays using SLIM}
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Abstract:


Normalization of microarray data is essential for coping with
experimental variations and revealing meaningful biological results.
We have developed a normalization procedure based on Semi-Linear
In-slide Model (SLIM), which adjusts objectively experimental
variations and are applicable to both cDNA microarrays and Affymetrix
oligonucleotide arrays.  This semiparametric model has a number of
interesting features, including the partial consistency phenomenon:
the parametric component and the nonparametric component of interest
can be consistently estimated, the former possessing parametric rate
and the latter having nonparametric rate, while the nuisance
parameters can not be consistently estimated.  The significant
analysis of gene expressions is based on a newly developed weighted
t-statistic, which accounts for the heteroscedasticity of the observed
log-ratios of expressions, and a balanced sign permutation test. We
illustrated the use of the newly developed techniques in a comparison
of the expression profiles of neuroblastoma cells that were stimulated
with a growth factor, macophage migration inhibitory factor.  The
results based on the Affymetrix microarrays with MIF deleted are also
presented.

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