A myriad of approaches have been developed for the analysis of genome-wide transcriptional profi ling data [121] [122] [123] [124] . However, there is no silver bullet when it comes to microarray data analysis. Th e challenges encountered are several fold: 1) dimensionality, or how to cope with the fact that the number of parameters measured exceeds by several orders of magnitude the number of conditions included in most experiments; 2) noise -a direct consequence of the fi rst point is that results from microarray analyses are particularly permissive to noise (false discovery); 3) 'seeing' the data -data visualization is critical as it helps promote insight and supports data interpretation; 4) biological context -it is important to keep the biology in sight at all times. Indeed, while it is easy to become absorbed by the data, it is essential to use biological knowledge when designing analysis strategies. Finally, there is hardly a one-size-fi ts-all approach to micro array data analysis and what works in one situation may not be universally applicable. Indeed, the most common response from experts when questioned on the best way to analyze a given dataset is that 'it depends…': it depends, for instance, on the extent of the diff erences being observed or on the variability inherent to a given disease or study population; it depends on what questions are being asked; or it can depend on whether follow-up confi rmatory experiments are planned. In Table 1 we provide a data mining primer that explains the basic steps involved in microarray data analysis and the considerations that arise [125] [126] [127] [128] [129] . Ad hoc data mining approaches can be developed to meet specifi c needs. For instance, we have developed a data mining strategy for the specifi c purpose of analyzing blood transcriptional profi les [15] . Th is approach simply consists of a priori grouping of sets of genes with similar transcriptional patterns. Th is is repeated for several diff erent datasets and subsequently, when comparing the cluster membership of all the genes across those datasets, the genes with similar membership Figure 4 . Data management is key to progress. Extensive cellular and molecular profi ling of human subjects generates vast amounts of disparate data. Eff ective data management and integration solutions are essential to the preservation of this information in an interpretable form. Thus, data management eff orts occurring 'behind the scenes' have an essential role to play in realizing the full potential of high throughput profi ling approaches in human subjects. are grouped together to form what we have termed a transcriptional module. Structuring the data permits focusing downstream statistical testing on these sets of transcripts that form coherent transcriptional and functional modular units. Th is is in contrast with more traditional approaches that rely on iterative statistical testing for thousands of individual transcripts that are treated as independent variables. Th e modular transcriptional framework that we have developed reduces the number of variables by collapsing sets of coordinately expressed genes into a new entity, the module. Reducing data dimensionality as such can: 1) facilitate functional inter pretation; 2) enable comparative analyses across multiple datasets and diseases; 3) minimize noise and improve robustness of biomarker signatures; and 4) yield multivariate metrics that can be used at the bedside [15] . Data visualization is also of critical importance for the interpretation of large-scale datasets. We have devised a straightforward visualization scheme for mapping global transcriptional changes for individual diseases on a modular basis ( Figure 5 ).Briefl y, diff erences in expression levels between study groups are displayed for each module on a grid. Each position on the grid is assigned to a given module; a red spot indicates an increase and a blue spot a decrease in transcript abundance. Th e spot intensity is determined by the proportion of transcripts reaching signifi cance for a given module. A posteriori, biological interpretation has linked several modules to immune cells or pathways (see legend of Figure 5 ). Hence, in the example provided in Figure 5 