Random matrix analysis of localization properties of Gene co-expression network

We analyze gene co-expression network under the random matrix theory framework. The nearest neighbor spacing distribution of the adjacency matrix of this network follows Gaussian orthogonal statistics of random matrix theory (RMT). Spectral rigidity test follows random matrix prediction for a certain range, and deviates after wards. Eigenvector analysis of the network using inverse participation ratio (IPR) suggests that the statistics of bulk of the eigenvalues of network is consistent with those of the real symmetric random matrix, whereas few eigenvalues are localized. Based on these IPR calculations, we can divide eigenvalues in three sets; (A) The non-degenerate part that follows RMT. (B) The non-degenerate part, at both ends and at intermediate eigenvalues, which deviate from RMT and expected to contain information about {\it important nodes} in the network. (C) The degenerate part with $zero$ eigenvalue, which fluctuates around RMT predicted value. We identify nodes corresponding to the dominant modes of the corresponding eigenvectors and analyze their structural properties

Application of Volcano Plots in Analyses of mRNA Differential Expressions with Microarrays

Volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log10(p-value) from the t test). We review the basic and an interactive use of the volcano plot, and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide an unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility to apply volcano plots to other fields beyond microarray.Comment: 8 figure

Understanding drugs in breast cancer through drug sensitivity screening

With substantial numbers of breast tumors showing or acquiring treatment resistance, it is of utmost importance to develop new agents for the treatment of the disease, to know their effectiveness against breast cancer and to understand their relationships with other drugs to best assign the right drug to the right patient. To achieve this goal drug screenings on breast cancer cell lines are a promising approach. In this study a large-scale drug screening of 37 compounds was performed on a panel of 42 breast cancer cell lines representing the main breast cancer subtypes. Clustering, correlation and pathway analyses were used for data analysis. We found that compounds with a related mechanism of action had correlated IC50 values and thus grouped together when the cell lines were hierarchically clustered based on IC50 values. In total we found six clusters of drugs of which five consisted of drugs with related mode of action and one cluster with two drugs not previously connected. In total, 25 correlated and four anti-correlated drug sensitivities were revealed of which only one drug, Sirolimus, showed significantly lower IC50 values in the luminal/ERBB2 breast cancer subtype. We found expected interactions but also discovered new relationships between

Progressive leukoencephalopathy impairs neurobehavioral development in sialin-deficient mice

Slc17a5−/− mice represent an animal model for the infantile form of sialic acid storage disease (SASD). We analyzed genetic and histological time-course expression of myelin and oligodendrocyte (OL) lineage markers in different parts of the CNS, and related this to postnatal neurobehavioral development in these mice. Sialin-deficient mice display a distinct spatiotemporal pattern of sialic acid storage, CNS hypomyelination and leukoencephalopathy. Whereas few genes are differentially expressed in the perinatal stage (p0), microarray analysis revealed increased differential gene expression in later postnatal stages (p10–p18). This included progressive upregulation of neuroinflammatory genes, as well as continuous down-regulation of genes that encode myelin constituents and typical OL lineage markers. Age-related histopathological analysis indicates that initial myelination occurs normally in hindbrain regions, but progression to more frontal areas is affected in Slc17a5−/− mice. This course of progressive leukoencephalopathy and CNS hypomyelination delays neurobehavioral development in sialin-deficient mice. Slc17a5−/− mice successfully achieve early neurobehavioral milestones, but exhibit progressive delay of later-stage sensory and motor milestones. The present findings may contribute to further understanding of the processes of CNS myelination as well as help to develop therapeutic strategies for SASD and other myelination disorders

FABIA: factor analysis for bicluster acquisition

Motivation: Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called ‘FABIA: Factor Analysis for Bicluster Acquisition’. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques

The Educational and Professional Background of Central Bankers and its Effect on Inflation - An Empirical Analysis

We assume that central banks can control inflation so that inflation rates reflect the preferences of the central bank council.The hypothesis to be tested is that these preferences depend on the central bankers? educational and/or professional background. In a panel data analysis for the euro area and eleven countries since 1973,we explain inflation first by the weights which the various educational and professional characteristics occupy in the central bank council and second by the education or profession of the median central bank council member. Our results indicate that, with regard to professional background, former members of the central bank staff as well as former bankers and businessmen have the strongest inflation aversion and that former trade unionists and politicians seem to have the highest inflation preference.As for the education of the council members, our results are less robust. However, if the median member of the central bank council has studied business, the inflation rate is significantly lower than if she has studied economics

Transcription profiles of the bacterium Mycoplasma pneumoniae grown at different temperatures

Applying microarray technology, we have investigated the transcriptome of the small bacterium Mycoplasma pneumoniae grown at three different temperature conditions: 32, 37 and 32°C followed by a heat shock for 15 min at 43°C, before isolating the RNA. From 688 proposed open-reading frames, 676 were investigated and 564 were found to be expressed (P < 0.001; 606 with P < 0.01) and at least 33 (P < 0.001; 77 at P < 0.01) regulated. By quantitative real-time PCR of selected mRNA species, the expression data could be linked to absolute molecule numbers. We found M.pneumoniae to be regulated at the transcriptional level. Forty-seven genes were found to be significantly up-regulated after heat shock (P < 0.01). Among those were the conserved heat shock genes like dnaK, lonA and clpB, but also several genes coding for ribosomal proteins and 10 genes of unassigned functions. In addition, 30 genes were found to be down-regulated under the applied heat shock conditions. Further more, we have compared different methods of cDNA synthesis (random hexamer versus gene-specific primers, different RNA concentrations) and various normalization strategies of the raw microarray data