A framework for list representation, enabling list stabilization through incorporation of gene exchangeabilities

Analysis of multivariate data sets from e.g. microarray studies frequently results in lists of genes which are associated with some response of interest. The biological interpretation is often complicated by the statistical instability of the obtained gene lists with respect to sampling variations, which may partly be due to the functional redundancy among genes, implying that multiple genes can play exchangeable roles in the cell. In this paper we use the concept of exchangeability of random variables to model this functional redundancy and thereby account for the instability attributable to sampling variations. We present a flexible framework to incorporate the exchangeability into the representation of lists. The proposed framework supports straightforward robust comparison between any two lists. It can also be used to generate new, more stable gene rankings incorporating more information from the experimental data. Using a microarray data set from lung cancer patients we show that the proposed method provides more robust gene rankings than existing methods with respect to sampling variations, without compromising the biological significance

Current evidence and opportunities in child and adolescent public mental health: a research review

Background A public mental health lens is increasingly required to better understand the complex and multifactorial influences of interpersonal, community and institutional systems on the mental health of children and adolescents. Methods This research review (1) provides an overview of public mental health and proposes a new interactional schema that can guide research and practice, (2) summarises recent evidence on public mental health interventions for children and adolescents, (3) highlights current challenges for this population that might benefit from additional attention and (4) discusses methodological and conceptual hurdles and proposes potential solutions. Results In our evidence review, a broad range of universal, selective and indicated interventions with a variety of targets, mechanisms and settings were identified, some of which (most notably parenting programmes and various school-based interventions) have demonstrated small-to-modest positive effects. Few, however, have achieved sustained mental health improvements. Conclusions There is an opportunity to re-think how public mental health interventions are designed, evaluated and implemented. Deliberate design, encompassing careful consideration of the aims and population-level impacts of interventions, complemented by measurement that embraces complexity through more in-depth characterisation, or ‘phenotyping’, of interpersonal and environmental elements is needed. Opportunities to improve child and adolescent mental health outcomes are gaining unprecedented momentum. Innovative new methodology, heightened public awareness, institutional interest and supportive funding can enable enhanced study of public mental health that does not shy away from complexity

A method for visual identification of small sample subgroups and potential biomarkers

In order to find previously unknown subgroups in biomedical data and generate testable hypotheses, visually guided exploratory analysis can be of tremendous importance. In this paper we propose a new dissimilarity measure that can be used within the Multidimensional Scaling framework to obtain a joint low-dimensional representation of both the samples and variables of a multivariate data set, thereby providing an alternative to conventional biplots. In comparison with biplots, the representations obtained by our approach are particularly useful for exploratory analysis of data sets where there are small groups of variables sharing unusually high or low values for a small group of samples.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS460 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org