Group‑wise ANOVA simultaneous component analysis for designed omics experiments Saccenti, Edoardo Smilde, Age K. Camacho Páez, José Analysis of variance Designed experiments Principal Component Analysis Sparsity Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretation 2019-04-01T06:33:17Z 2019-04-01T06:33:17Z 2018-05 info:eu-repo/semantics/article Saccenti, E., Smilde, A. K., & Camacho, J. (2018). Group-wise ANOVA simultaneous component analysis for designed omics experiments. Metabolomics : Official journal of the Metabolomic Society, 14(6), 73. doi:10.1007/s11306-018-1369-1 http://hdl.handle.net/10481/55294 https://doi.org/10.1007/s11306-018-1369-1 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España