OVERVIEW

[1] Conesa, A.; Nueda, M.J.; Ferrer, A. and Talón, M. (2006) maSigPro: a Method to Identify Significantly Differential Expression Profiles in Time-Course Microarray Experiments. Bioinformatics, 22 (9), 1096-1102.
[2] Nueda, M.J.; Conesa, A.; Westerhuis, J.A.; Hoefsloot, H.C.J.; Smilde, A.K.; Talón, M. and Ferrer, A. (2007) Discovering gene expression patterns in Time Course Microarray Experiments by ANOVA-SCA. Bioinformatics, 23 (14), 1792-1800.
[3] Smilde, A.K.; Jansen, J.J.; Hoefsloot, H.C.J.; Lamers, R.J.A.N.; Van der Greef, J. and Timmerman, M.E. (2005) ANOVA-Simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data. Bioinformatics, 21 (13), 3043-3048.
[4] Nueda, M.J.; Sebastián, P.; Tarazona, S.; García-García, F.; Dopazo, J.; Ferrer, A. and Conesa, A. (2009) Functional Assessment of Time Course Microarray data. BMC Bioinformatics, 10 (suppl 6): S9.
[5] Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A, 25;102(43):15545-50.
[6] Al-Shahrour F, Arbiza L, Dopazo H, Huerta-Cepas J, Mínguez P, Montaner D, Dopazo J. (2007) From genes to functional classes in the study of biological systems. BMC Bioinformatics, 3;8:114.
[7] Tarazona S, Prado-López S, Dopazo J, Ferrer A, and Conesa A (2012). Variable selection for multifactorial genomic data. Chemometrics and Intelligent Laboratory Systems, 110:113-122.​