ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

Background: Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks. Results: We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining. Conclusion: ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.

Antioxidants attenuate hyperglycaemia-mediated brain endothelial cell dysfunction and blood–brain barrier hyperpermeability

Aims: Hyperglycaemia (HG), in stroke patients, is associated with worse neurological outcome by compromising endothelial cell function and the blood–brain barrier (BBB) integrity. We have studied the contribution of HG-mediated generation of oxidative stress to these pathologies and examined whether antioxidants as well as normalization of glucose levels following hyperglycaemic insult reverse these phenomena. Methods: Human brain microvascular endothelial cell (HBMEC) and human astrocyte co-cultures were used to simulate the human BBB. The integrity of the BBB was measured by transendothelial electrical resistance using STX electrodes and an EVOM resistance meter, while enzyme activities were measured by specific spectrophotometric assays. Results: After 5 days of hyperglycaemic insult, there was a significant increase in BBB permeability that was reversed by glucose normalization. Co-treatment of cells with HG and a number of antioxidants including vitamin C, free radical scavengers and antioxidant enzymes including catalase and superoxide dismutase mimetics attenuated the detrimental effects of HG. Inhibition of p38 mitogen-activated protein kinase (p38MAPK) and protein kinase C but not phosphoinositide 3 kinase (PI3 kinase) also reversed HG-induced BBB hyperpermeability. In HBMEC, HG enhanced pro-oxidant (NAD(P)H oxidase) enzyme activity and expression that were normalized by reverting to normoglycaemia. Conclusions: HG impairs brain microvascular endothelial function through involvements of oxidative stress and several signal transduction pathways.

Parcellation of fMRI datasets with ICA and PLS: a data driven approach

Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM) and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. Finally, we parcellate all subjects data with a spectral clustering of the PLS latent variables. We present results of the application of the proposed method on both single-subject and multi-subject fMRI datasets. Preliminary experimental results, evaluated with intra-parcel variance of GLM t-values and PLS derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over GLM based techniques.