Transcriptomic profiling experiments that aim to the identification of responsive genes in specific biological conditions are commonly set up under defined experimental designs that try to assess the effects of factors and their interactions on gene expression. Data from these controlled experiments, however, may also contain sources of unwanted noise that can distort the signal under study, affect the residuals of applied statistical models, and hamper data analysis. Commonly, normalization methods are applied to transcriptomics data to remove technical artifacts, but these are normally based on general assumptions of transcript distribution and greatly ignore both the characteristics of the experiment under consideration and the coordinative nature of gene expression. In this paper, we propose a novel methodology, ARSyN, for the preprocessing of microarray data that takes into account these 2 last aspects. By combining analysis of variance (ANOVA) modeling of gene expression values and multivariate analysis of estimated effects, the method identifies the nonstructured part of the signal associated to the experimental factors (the noise within the signal) and the structured variation of the ANOVA errors (the signal of the noise). By removing these noise fractions from the original data, we create a filtered data set that is rich in the information of interest and includes only the random noise required for inferential analysis. In this work, we focus on multifactorial time course microarray (MTCM) experiments with 2 factors: one quantitative such as time or dosage and the other qualitative, as tissue, strain, or treatment. However, the method can be used in other situations such as experiments with only one factor or more complex designs with more than 2 factors. The filtered data obtained after applying ARSyN can be further analyzed with the appropriate statistical technique to obtain the biological information required. To evaluate the performance of the filtering strategy, we have applied different statistical approaches for MTCM analysis to several real and simulated data sets, studying also the efficiency of these techniques. By comparing the results obtained with the original and ARSyN filtered data and also with other filtering techniques, we can conclude that the proposed method increases the statistical power to detect biological signals, especially in cases where there are high levels of structural noise. Software for ARSyN is freely available at http://www.ua.es/personal/mj.nueda.