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\begin{center}
{\Large\bf References}
\end{center}

\begin{enumerate}

\item Benjamin Y, Hochberg Y (1995) 
Controlling the false discovery rate: a practical and powerful approach to multiple testing. 
\textit{J Royal Statistical Society}, Series B 57: 289

\item Benjamini Y, Yekutieli D (2001)
The control of the false discovery rate in 
multiple tesing under dependency. Ann. Stat. 2001, 29:1165-8.

\item Churchill GA (2002) Fundamentals of experimental design for cDNA microarrays.
\textit{Nat Genet} 2002, 32 Suppl 2:490-5.

\item Cui XQ, Kerr MK, Churchill GA (2002) (in prep)
Data transformations for normalization of cDNA microarray data.

\item Cui XQ, Churchill GA (2003a) (in prep)
Statistical Tests for Differential Expression in cDNA Microarray Experiments

\item Cui XQ, Churchill GA (2003b) (in prep)
How Many Mice and How Many Arrays? 
Replication in Mouse cDNA Microarray Experiments,

\item Cui XQ, Hwang G, Qiu J, Blades N and Churchill GA (2003c), 
Improved Statistical Tests for Differential Gene Expression 
by Shrinking Variance Components, submitted.


\item Felsenstein J (1985) Confidence limits on phylogenies
-- An approach using the bootstrap. \textit{Evolution} 39:783

\item Kerr MK, Afshari CA, Bennett L, Bushel P, Martinez J, 
Walker N, Churchill GA, 
Statistical analysis of a gene expression microarray 
experiment with replication.
\textit{Statistica Sinica} 12:203

\item Kerr MK, Churchill, G.A. (2001a)
Experimental design for gene expression microarrays.
\textit{Biostatistics} 2:183

\item Kerr MK, Churchill, G.A. (2001b)
Statistical design and the analysis of gene expression microarray data.
\textit{Genetical Research} 77:123

\item Kerr MK and Churchill GA (2001c)
Bootstrapping cluster analysis: Assessing the reliability of conclusion from
microarray experiments.  \textit{PNAS} 98:8961

\item Kerr MK, Martin, M., Churchill, G.A. (2000)
Analysis of variance for gene expression microarray data.
\textit{J Computational Biology} 7:819

\item Kerr MK, Leiter EH, Picard L, Churchill GA (2002)
Sources of Variation in Microarray Experiments.
 In:
Computational and Statistical Approaches to Genomics, edited by
Wei Zhang and Ilya Shmulevich
Kluwer Academic Publishers, pp 41

\item Littell, RC, Milliken, GA, Stroup, WW and Wolfinger RD (1996), 
SAS system for mixed models (Cary, NC, SAS institute Inc.)

\item Margush, T. and F. R. McMorris (1981) Consensus n-trees.
\textit{Bulletin of Mathematical Biology} 43: 239

\item McLean RA, Sanders WL, Stroup WW (1991), A Unified Approach to
Mixed Linear Models, \textit{The American Statistician}, 45:54-64

\item Prichard CC, Hsu L, Delrow J and Nelson PS (2001), 
Project normal: defining normal variance in mouse gene expression, 
\textit{PNAS}, 98:13266

\item Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P,
Ashfari C, Paules RS (2001)
Assessing gene significance from cDNA microarray
expression data via mixed models.
\textit{J Comp Biol} 8:625

\item Searle SR, Casella G and McCulloch CE (1992), Variance components, 
John Wiley and sons, Inc.

\item SAS/STAT User's Guide (1999), SAS Institute Inc.

\item Witkovsky V. (2001), MATLAB algorithm mixed.m for solving Henderson's
mixed model equations, Technical Report, Inst. of Measurement Science,
Slovak Academy of Science
\item Wu H., Kerr MK, Cui X, Churchill GA, MAANOVA: A A Software Package for the
Analysis of Spotted cDNA Microarray Experiments, 
In: The analysis of gene expression data: methods and software,
In press, Springer

\item Yang YH, Dudoit S, Luu P, Lin DM Peng V, Ngai J, Speed TP. (2001)
Normalization for cDNA microarray data:  a robust composite method addressing
single and multiple slide systematic variation.
\textit{Nucleic Acids Research} 2002 30:e15.


\end{enumerate}

