From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data

Background: The use of correlation networks is widespread in the analysis of gene expression and proteomics data, even though it is known that correlations not only confound direct and indirect associations but also provide no means to distinguish between cause and effect. For "causal" analysis typically the inference of a directed graphical model is required. However, this is rather difficult due to the curse of dimensionality. Results: We propose a simple heuristic for the statistical learning of a high-dimensional "causal" network. The method first converts a correlation network into a partial correlation graph. Subsequently, a partial ordering of the nodes is established by multiple testing of the log-ratio of standardized partial variances. This allows identifying a directed acyclic causal network as a subgraph of the partial correlation network. We illustrate the approach by analyzing a large Arabidopsis thaliana expression data set. Conclusion: The proposed approach is a heuristic algorithm that is based on a number of approximations, such as substituting lower order partial correlations by full order partial correlations. Nevertheless, for small samples and for sparse networks the algorithm not only yield sensible first order approximations of the causal structure in high-dimensional genomic data but is also computationally highly efficient. Availability and Requirements: The method is implemented in the "GeneNet" R package (version 1.2.0), available from CRAN and from http://strimmerlab.org/software/genets/. The software includes an R script for reproducing the network analysis of the Arabidopsis thaliana data

Inference of demographic history from genealogical trees using reversible jump Markov chain Monte Carlo

Background: Coalescent theory is a general framework to model genetic variation in a population. Specifically, it allows inference about population parameters from sampled DNA sequences. However, most currently employed variants of coalescent theory only consider very simple demographic scenarios of population size changes, such as exponential growth. Results: Here we develop a coalescent approach that allows Bayesian non-parametric estimation of the demographic history using genealogies reconstructed from sampled DNA sequences. In this framework inference and model selection is done using reversible jump Markov chain Monte Carlo (MCMC). This method is computationally efficient and overcomes the limitations of related non-parametric approaches such as the skyline plot. We validate the approach using simulated data. Subsequently, we reanalyze HIV-1 sequence data from Central Africa and Hepatitis C virus (HCV) data from Egypt. Conclusions: The new method provides a Bayesian procedure for non-parametric estimation of the demographic history. By construction it additionally provides confidence limits and may be used jointly with other MCMC-based coalescent approaches

Stimuluskomplexgrad-abhängige konditionierte Hemmung und latente Inhibition

Hintergrund: Latente Inhibition (LI) und Konditionierte Hemmung (conditioned blocking = CB) wurden vielfach zur Erfassung von Aufmerksamkeitsmechanismen im Tiermodell und bei gesunden und psychiatrisch erkrankten Menschen untersucht und dienen in der Grundlagenforschung und bei psychopharmakologischen Fragestellungen als Modell für die Aufmerksamkeitsgenerierung. Fragestellung: Die bisher stets isoliert untersuchten Paradigmen sollten in vorliegender Arbeit in einem gemeinsamen Versuchsdesign erfasst werden, um klinische Untersuchungen defizitärer Bereiche der Aufmerksamkeitssteuerung, z. B. bei schizophrenen Patienten, effizienter durchführen zu können und die Abhängigkeit der Phänomene von der Komplexität der visuellen Reizinformationsmenge zu untersuchen. Methoden: Durchführung eines computergestützten Tests zur visuellen Suche mit 4 unterschiedlichen Reizkomplexitätsgraden unter Li- und CB- Bedingungen bei 30 gesunden Probanden Ergebnisse: Bei Verwendung einfacher Reize ließen sich in der verwendeten Reaktionszeitaufgabe keine Lerneffekte nachweisen. Bei Verwendung von Reizen mittlerer Informationsmenge waren sowohl der LI-Effekt als auch der CB-Effekt demonstrierbar. Bei größerer Reizkomplexität war ein CB-Effekt, jedoch kein LI-Effekt nachweisbar. Bei der Kategorie der höchsten Komplexität fand sich ein statistisch signifikanter LI-Effekt, jedoch kein CB-Effekt. Die Verwendung dieser Reizkategorie als höchste Komplexitätsstufe ist aufgrund der wahrscheinlich distinkten neuronalen Verarbeitungsmechanismen kritisch zu betrachten. Schlussfolgerung: Effekte erlernter Unaufmerksamkeit konnten mit dem vorgestellten Versuchsdesign dargestellt werden. Unterschiede ergeben sich in Abhängigkeit vom Informationsgehalt des Reizmaterials. Der Test bietet ein einfaches und durchführbares Modell z. B. für die Untersuchung von Aufmerksamkeitsdefiziten bei schizophrenen Patienten als reproduzierbares neuropsychologisches klinisches Merkmal und ist für die Untersuchung von Auswirkungen medikamentöser Behandlung auf Aufmerksamkeitsprozesse geeignet

Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process

Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning these networks is challenging due to the low sample size and high dimensionality of genomic data. Results: We present a novel and highly efficient approach to estimate a VAR network. This proceeds in two steps: (i) improved estimation of VAR regression coefficients using an analytic shrinkage approach, and (ii) subsequent model selection by testing the associated partial correlations. In simulations this approach outperformed for small sample size all other considered approaches in terms of true discovery rate (number of correctly identified edges relative to the significant edges). Moreover, the analysis of expression time series data from Arabidopsis thaliana resulted in a biologically sensible network. Conclusion: Statistical learning of large-scale VAR causal models can be done efficiently by the proposed procedure, even in the difficult data situations prevalent in genomics and proteomics. Availability: The method is implemented in R code that is available from the authors on request

Inference of Temporally Varying Bayesian Networks

When analysing gene expression time series data an often overlooked but crucial aspect of the model is that the regulatory network structure may change over time. Whilst some approaches have addressed this problem previously in the literature, many are not well suited to the sequential nature of the data. Here we present a method that allows us to infer regulatory network structures that may vary between time points, utilising a set of hidden states that describe the network structure at a given time point. To model the distribution of the hidden states we have applied the Hierarchical Dirichlet Process Hideen Markov Model, a nonparametric extension of the traditional Hidden Markov Model, that does not require us to fix the number of hidden states in advance. We apply our method to exisiting microarray expression data as well as demonstrating is efficacy on simulated test data

Exome sequencing helped the fine diagnosis of two siblings afflicted with atypical Timothy syndrome (TS2)

BACKGROUND: Long-QT syndrome (LQTS) causes a prolongation of the QT-interval in the ECG leading to life threatening tachyarrhythmia and ventricular fibrillation. One atypical form of LQTS, Timothy syndrome (TS), is associated with syndactyly, immune deficiency, cognitive and neurological abnormalities as well as distinct cranio-facial abnormalities. CASE PRESENTATION: On a family with both children diagnosed with clinical LQTS, we performed whole exome sequencing to comprehensively screen for causative mutations after a targeted candidate gene panel screen for Long-QT syndrome target genes failed to identify any underlying genetic defect. Using exome sequencing, we identified in both affected children, a p.402G > S mutation in exon 8 of the CACNA1C gene, a voltage-dependent Ca2+ channel. The mutation was inherited from their father, a mosaic mutation carrier. Based on this molecular finding and further more careful clinical examination, we refined the diagnosis to be Timothy syndrome (TS2) and thereby were able to present new therapeutic approaches. CONCLUSIONS: Our study highlights the difficulties in accurate diagnosis of patients with rare diseases, especially those with atypical clinical manifestation. Such challenge could be addressed with the help of comprehensive and unbiased mutation screening, such as exome sequencing

Gene network reconstruction from microarray data

<p>Abstract</p> <p>Background</p> <p>Often, software available for biological pathways reconstruction rely on literature search to find links between genes. The aim of this study is to reconstruct gene networks from microarray data, using Graphical Gaussian models.</p> <p>Results</p> <p>The <it>GeneNet </it>R package was applied to the Eadgene chicken infection data set. No significant edges were found for the list of differentially expressed genes between conditions MM8 and MA8. On the other hand, a large number of significant edges were found among 85 differentially expressed genes between conditions MM8 and MM24.</p> <p>Conclusion</p> <p>Many edges were inferred from the microarray data. Most of them could, however, not be validated using other pathway reconstruction software. This was partly due to the fact that a quite large proportion of the differentially expressed genes were not annotated. Further biological validation is therefore needed for these networks, using for example in vitro invalidation of genes.</p

Probabilistic modeling and machine learning in structural and systems biology

This supplement contains extended versions of a selected subset of papers presented at the workshop PMSB 2007, Probabilistic Modeling and Machine Learning in Structural and Systems Biology, Tuusula, Finland, from June 17 to 18, 2006

Compensatory upregulation of anti-beta-adrenergic receptor antibody levels might prevent heart failure presentation in pediatric myocarditis

BACKGROUND: Myocarditis can be associated with severe heart failure and is caused by different inflammatory and autoimmune responses. The aim of this study was to describe the immunological response in children with myocarditis by analyzing anti-beta-adrenergic receptor antibodies (anti-β-AR Abs). METHODS: Sera of children who were hospitalized with biopsy-proven myocarditis were prospectively collected between April 2017 and March 2019. Anti-β1-AR Ab, anti-β2-AR Ab, and anti-β3-AR Ab were quantified by a CE-certified ELISA kit. According to normal values for immunoglobulin G (IgG), three age groups, 5–17 years, were defined. Children without inflammatory cardiac pathology and no heart failure signs were served as a control group. RESULTS: We compared 22 patients with biopsy-proven myocarditis and 28 controls. The median age (interquartile range) of the myocarditis group (MYC) was 12.1 (2.7–16.4) years, 13 men, left ventricular ejection fraction (LVEF) 51% and for control group, the median age was 5.0 (3.0–6.8) years, nine men, LVEF 64%. Myocarditis patients in the age group >5–17 years showed significantly higher anti-β3-AR Ab levels as compared to controls (p = 0.014). Lower anti-β2-AR Ab and anti-β3-AR Ab levels were significantly correlated with higher left ventricular diameters in myocarditis patients. The event-free survival using a combined endpoint (mechanical circulatory support [MCS], transplantation, and/or death) was significantly lower in myocarditis patients with antibody levels below the median as compared to myocarditis patients with antibody levels ≥ the median. CONCLUSION: Anti-β-AR Ab levels are increased in children with myocarditis and >5 years of age. These antibodies might be upregulated compensatory to prevent further cardiac deterioration. A worse event-free survival in patients with lower anti-β-AR Ab levels might be a therapeutic target for immunoglobulin substitution