Open Access
Issue
Genet. Sel. Evol.
Volume 39, Number 6, November-December 2007
Page(s) 633 - 650
DOI https://doi.org/10.1051/gse:2007029
Published online 06 December 2007
References of  Genet. Sel. Evol. 39 (2007) 633-650
  1. Baird D., Johnstone P., Wilson T., Normalization of microarray data using a spatial mixed model analysis which includes splines, Bioinformatics 20 (2004) 3196-3205 [CrossRef] [PubMed].
  2. Benjamini Y., Hochberg Y., Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc. B 85 (1995) 289-300.
  3. Dabney A.R., Leek J.T., Monsen E., Storey J.D., Edge manual, Department of Biostatistics, University of Washington, http://faculty.washington.edu/jstorey/edge, 2006.
  4. Déjean S., Martin P.G.P., Baccini A., Besse P., Clustering time series gene expression data using smoothing spline derivatives, EURASIP J. Bioinformatics Syst. Biol. (2007) ID 70561.
  5. Gibson G., Wolfinger R.D., Gene expression profiling using mixed models, in: Saxton A.M. (Ed.), Genetic analysis of complex trait using SAS $^\circledR$, SAS $^\circledR$ User Press, Cary NC, 2004, Chap. 11, pp. 251-278.
  6. Ishwaran H., Rao J.S., Kogalur U.B., BAMarrayTM: Java software for Bayesian analysis of variance for microarray data, BMC Bioinformatics 7 (2006) 59 [CrossRef] [PubMed].
  7. Jaffrézic F., Marot G., Degrelle S., Hue I., Foulley J.-L., A structural mixed model for variances in differential gene expression studies, Genet. Res. 89 (2007) 19-25 [CrossRef] [PubMed].
  8. Pool M.H., Hulsegge B., Janss L.L.G., Background bias on cDNA micro-arrays, EAAP, Uppsala, Sweden, 2005.
  9. Ritchie M.E., Diyagama D., Neilson J., van Laar R., Dobrovic A., Holloway A., Smyth G.K., Empirical array quality weights for microarray data, BMC Bioinformatics 7 (2006) 261 [CrossRef] [PubMed], http://www.biomedcentral.com/1471-2105/7/261
  10. Robert-Granié C., Baccini A., Besse P., Déjean S., Ferré P.J., Liaubet L., Martin P.G.P., San Cristobal M., Kinetics analysis of microarray data using semiparametric mixed models, 8th World Congress on Genetics Applied to Livestock Production, Belo-Horizonte, Brazil, August 13-18, 2006.
  11. Saeed A.I., Sharov V., White J., Li J., Liang W., Bhagabati N., Braisted J., Klapa M., Currier T., Thiagarajan M., Sturn A., Snuffin M., Rezantsev A., Popov D., Ryltsov A., Kostukovich E., Borisovsky I., Liu Z., Vinsavich A., Trush V., Quackenbush J., TM4: a free, open-source system for microarray data management and analysis, Biotechniques 34 (2003) 374-378 [PubMed].
  12. Smyth G.K., Linear models and empirical Bayes methods for assessing differential expression in microarray experiments, Statist. Appl. Genet. Mol. Biol. 3 (2004) 3.
  13. Smyth G.K., Limma: linear models for microarray data, in: Gentleman R.C., Carey V.J., Dudoit S., Irizarry R., Huber W. (Eds.), Bioinformatics and Computional Biology using R and Biocondutor, Springer, New York, 2005, pp. 397-420.
  14. Smyth G.K., Speed T., Normalization of cDNA microarray data, Methods 31 (2003) 265-273 [CrossRef] [PubMed].
  15. Wettenhall J.M., Smyth G.K., LimmaGUI: A graphical user interface for linear modeling of microarray data, Bioinformatics 20 (2004) 3705-3706 [CrossRef] [PubMed].
  16. Wolfinger R.D., Gibson G., Wolfinger E.D., Bennett L., Hamadeh H., Bushel P., Afshari C., Paules R.S., Assessing gene significance from cDNA microarray expression data via mixed models, J. Comp. Biol. 8 (2001) 625-637.
  17. Yang Y., Dudoit S., Luu P., Peng V., Ngai J., Speed T., Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation, Nucleic Acids Res. 30 (2002) e15.