@article{
BH,
   Author = {Benjamini, Y. and Hochberg, Y.},
   Title = {Controlling the false discovery rate: a practical and powerful approach to multiple testing},
   Journal = {Journal of the Royal Statistical Society},
   Volume = {Ser. B 57},
   Pages = {289-300},
      Year = {1995} }



@article{
BY,
   Author = {Benjamini, Y. and Yekutieli, D.},
   Title = {The control of the false discovery rate in multiple testing under dependency},
   Journal = {Ann. Stat.},
   Volume = {29},
   Number = {4},
   Pages = {1165–1188},
      Year = {2001} }

@article{
MIAME,
   Author = {Brazma, A. and Hingamp, P. and Quackenbush, J. and Sherlock, G. and Spellman, P. and Stoeckert, C. and Aach, J. and Ansorge, W. and Ball, C. A. and Causton, H. C. and Gaasterland, T. and Glenisson, P. and Holstege, F. C. and Kim, I. F. and Markowitz, V. and Matese, J. C. and Parkinson, H. and Robinson, A. and Sarkans, U. and Schulze-Kremer, S. and Stewart, J. and Taylor, R. and Vilo, J. and Vingron, M.},
   Title = {Minimum information about a microarray experiment (MIAME)-toward standards for microarray data},
   Journal = {Nat Genet},
   Volume = {29},
   Number = {4},
   Pages = {365-71},
   Note = {1061-4036 (Print)
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.},
   Abstract = {Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.},
   Keywords = {*Computational Biology
Gene Expression Profiling/methods
Oligonucleotide Array Sequence Analysis/*standards},
   Year = {2001} }

@article{
Bioconductor,
   Author = {Gentleman, R. C. and Carey, V. J. and Bates, D. M. and Bolstad, B. and Dettling, M. and Dudoit, S. and Ellis, B. and Gautier, L. and Ge, Y. and Gentry, J. and Hornik, K. and Hothorn, T. and Huber, W. and Iacus, S. and Irizarry, R. and Leisch, F. and Li, C. and Maechler, M. and Rossini, A. J. and Sawitzki, G. and Smith, C. and Smyth, G. and Tierney, L. and Yang, J. Y. and Zhang, J.},
   Title = {Bioconductor: open software development for computational biology and bioinformatics},
   Journal = {Genome Biol},
   Volume = {5},
   Number = {10},
   Pages = {R80},
   Note = {1465-6914
Journal Article},
   Abstract = {The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.},
   Keywords = {Computational Biology/*instrumentation/*methods
Internet
Reproducibility of Results
*Software},
   Year = {2004} }

@article{
Hochberg,
   Author = {Hochberg, Y.},
   Title = {A sharper Bonferroni procedure for multiple tests of significance},
   Journal = {Biometrika},
   Volume = {75},
   Pages = {800-802},
      Year = {1988} }

@article{
Holm,
   Author = {Holm, S.},
   Title = {A simple sequentially rejective multiple test procedure},
   Journal = {Scandinavian Journal of Statistics},
   Volume = {6},
   Pages = {65–70},
      Year = {1979} }

@article{
LEE,
   Author = {Lee, M. L. and Kuo, F. C. and Whitmore, G. A. and Sklar, J.},
   Title = {Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations},
   Journal = {Proc Natl Acad Sci U S A},
   Volume = {97},
   Number = {18},
   Pages = {9834-9},
   Note = {0027-8424 (Print)
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.},
   Abstract = {We present statistical methods for analyzing replicated cDNA microarray expression data and report the results of a controlled experiment. The study was conducted to investigate inherent variability in gene expression data and the extent to which replication in an experiment produces more consistent and reliable findings. We introduce a statistical model to describe the probability that mRNA is contained in the target sample tissue, converted to probe, and ultimately detected on the slide. We also introduce a method to analyze the combined data from all replicates. Of the 288 genes considered in this controlled experiment, 32 would be expected to produce strong hybridization signals because of the known presence of repetitive sequences within them. Results based on individual replicates, however, show that there are 55, 36, and 58 highly expressed genes in replicates 1, 2, and 3, respectively. On the other hand, an analysis by using the combined data from all 3 replicates reveals that only 2 of the 288 genes are incorrectly classified as expressed. Our experiment shows that any single microarray output is subject to substantial variability. By pooling data from replicates, we can provide a more reliable analysis of gene expression data. Therefore, we conclude that designing experiments with replications will greatly reduce misclassification rates. We recommend that at least three replicates be used in designing experiments by using cDNA microarrays, particularly when gene expression data from single specimens are being analyzed.},
   Keywords = {DNA, Complementary/*genetics
Gene Expression Profiling/*methods
*Gene Expression Regulation
*Models, Statistical
*Oligonucleotide Array Sequence Analysis
Probability
RNA, Messenger/*genetics
Reproducibility of Results
Statistics/methods},
   Year = {2000} }

