\magnification=1200
\baselineskip=20pt
\nopagenumbers
\font\big=cmr12 scaled \magstep2

\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big JOINT PROBABILITY / STATISTICS SEMINAR}
\bigskip

\baselineskip=12pt
\centerline{4:30 p.m., Wednesday, January 15, 2003}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 4:00 in 1st Floor Lounge)}

\bigskip
\baselineskip=15pt
\centerline{\sl Agnes Hsiung}
\centerline{\sl Division of Biostatistics and Bioinformatics}
\centerline{\sl National Health Research Institutes, Taiwan}
\bigskip
\centerline{\bf Normalization for cDNA Microarray Experiments having many
Differentially Expressed Genes}
\bigskip

This talk concerns normalization of cDNA microarray data in which a
substantial proportion of genes differ in expression between the two
mRNA samples, or there is no symmetry in the expression levels of
up/down-regulated genes, and there are no control DNA sequences on the
slide. The first step of this approach is to perform global
normalization based on dye-swap experiments, and then use a statistical
criterion to select a set of (almost) constantly expressed genes. Based
on this set, intensity dependent normalization is carried out using
local regression method. The usefulness of this method is clearly
demonstrated in simulation studies and in the analysis of real data
sets. In particular, it is shown in the simulation studies that this
method identifies genes with a lower false positive rate and a lower
false negative rate than a commonly used method, when a large number of
genes are turned up or down.

\bye