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Statistical methodology for the analysis of dye-switch microarray experiments

By Tristan Mary-Huard, Julie Aubert, Nadera Mansouri-Attia, Olivier Sandra and Jean-Jacques Daudin
Topics: Methodology Article
Publisher: BioMed Central
OAI identifier: oai:pubmedcentral.nih.gov:2277403
Provided by: PubMed Central

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Citations

  1. (2002). Design issues for cDNA microarray experiments. Nat Rev Genet
  2. (2002). G: Fundamentals of experimental design for cDNA microarrays. Nat Genet
  3. (2002). Y a n g Y , D u d o i t S , L u u P , S p e e d T : Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nuclear Acids Res
  4. (2002). Churchill G: Statistical Analysis of a gene expression microarray experiment with replication. Statistica Sinica
  5. (2003). R: Statistical design of reverse dye microarrays. Bioinformatics
  6. (2005). Evaluation of the gene-specific dye bias in cDNA microarray experiments. Bioinformatics
  7. (2005). jj j n ij i j i −
  8. (2002). Variation in gene expression within and among natural populations. Nat Genet
  9. (2006). Neutral and adaptive variation in gene expression.
  10. (2000). Analysis of variance for gene expression microarray data.
  11. (2004). Statistics for Microarrays: Design, Analysis and Inference Chichester:
  12. (2004). Efficient two-sample designs for microarray experiments with biological replications. Silico Biology
  13. (2006). Efficient design and analysis of two colour factorial microarray experiments.
  14. (2007). JP R: Identification of differentially regulated genes in the endometrium of cyclic and pregnant cows using a high-throughput transcriptome analysis.
  15. (2005). Near-optimal designs for dual channel microarray studies.
  16. (2001). A: A Bayesian Framework for the Analysis of Microarray Expression Data: Regularized t-Test and Statistical Inferences of Gene Changes. Bioinformatics
  17. (2005). VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data. Bioinformatics
  18. (2001). Statistical Applications in Genetics and Molecular Biology 2004, 3:Article 3. 19. Tusher V, Tibshirani R, Chu C: Significance analysis of microarrays applied to transcriptional response to ionizing radiations. PNAS

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