Unsupervised assessment of microarray data quality using a Gaussian mixture model

<p>Abstract</p> <p>Background</p> <p>Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny.</p> <p>Results</p> <p>We show how an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and the naïve Bayes model can be used to automate microarray quality assessment. The method is flexible and can be easily adapted to accommodate alternate quality statistics and platforms. We evaluate our approach using Affymetrix 3' gene expression and exon arrays and compare the performance of this method to a similar supervised approach.</p> <p>Conclusion</p> <p>This research illustrates the efficacy of an unsupervised classification approach for the purpose of automated microarray data quality assessment. Since our approach requires only unannotated training data, it is easy to customize and to keep up-to-date as technology evolves. In contrast to other "black box" classification systems, this method also allows for intuitive explanations.</p

Is cognitive lifestyle associated with depressive thoughts and self-reported depressive symptoms in later life?

© 2015, The Author(s). Key components of cognitive lifestyle are educational attainment, occupational complexity and engagement in cognitively stimulating leisure activities. Each of these factors is associated with experiencing fewer depressive symptoms in later life, but no study to date has examined the relationship between overall cognitive lifestyle and depressive symptoms. This task is made more complex because relatively few older participants in cross-sectional studies will be currently experiencing depression. However, many more will show evidence of a depressive thinking style that predisposes them towards depression. This study aimed to investigate the extent to which cognitive lifestyle and its individual components are associated with depressive thoughts and symptoms. Two hundred and six community-dwelling participants aged 65+ completed the depressive cognitions scale, the geriatric depression scale and the lifetime of experiences questionnaire, which assesses cognitive lifestyle. Correlational analysis indicated that each of the individual lifestyle factors—education, occupational complexity and activities in young adulthood, mid-life and later life—and the combined cognitive lifestyle score was positively associated with each other and negatively with depressive symptoms, while all except education were negatively associated with depressive thoughts. Depressive thoughts and symptoms were strongly correlated. Cognitive lifestyle score explained 4.6 % of the variance in depressive thoughts and 10.2 % of the variance in depressive symptoms. The association of greater participation in cognitive activities, especially in later life, with fewer depressive symptoms and thoughts suggests that preventive interventions aimed at increasing participation in cognitively stimulating leisure activity could be beneficial in decreasing the risk of experiencing depressive thoughts and symptoms in later life