Erzeugung von positiv definiten Matrizen mit Nebenbedingungen zur Validierung von Netzwerkalgorithmen für Microarray-Daten

Microarray-Daten werden in letzter Zeit häufig genutzt, um mit Hilfe verschiedener Verfahren Netzwerke der Gen-Gen-Interaktion zu generieren. Die vorliegende Arbeit beschäftigt sich mit Validierungsstudien solcher Verfahren. Der Startpunkt einer Validierungsstudie ist ein ungerichteter Graph, der biologische Strukturen repräsentieren soll. In dieser Arbeit wird motiviert, Graphen zu benutzen, die aus Microarray-Daten geschätzt worden sind. Nachdem ein Graph gewählt worden ist, werden Daten einer multivariaten Normalverteilung erzeugt, die durch eine zufällige Kovarianzmatrix charakterisiert ist. Diese Matrix muss symmetrisch und positiv definit sein, aber zusätzlich wird für eine nicht vorhandene Kante im Graphen gefordert, dass der zugehörige Eintrag in der Matrix Null ist. In dieser Arbeit wird ein neuer Ansatz vorgestellt, der es ermöglicht, symmetrische, positiv definite Matrizen mit Nebenbedingungen zu erzeugen. Diese Methode beruht auf der Moralisierung eines Graphen. Ein gerichteter, azyklischer Graph wird moralisiert, indem die gerichteten Kanten durch ungerichtete Kanten ersetzt werden und zusätzlich die Eltern eines jeden Knotens paarweise miteinander verbunden werden. Der zentrale Schritt bei der Erstellung der Matrizen mit Nebenbedingungen liegt in der Umkehrung des Moralisierungsvorganges. In dieser Arbeit wird die Klasse der Graphen eingeführt, die Resultat einer Moralisierung sein könnten - die prämoralisierbaren Graphen - und es wird ein Verfahren definiert, welches entscheidet, ob ein Graph prämoralisierbar ist und gegebenenfalls eine Umkehrung der Moralisierung durchführt. Die erzeugten Matrizen sollen als Korrelationsmatrizen für die Validierungsstudien genutzt werden. Dazu wird das vorgestellte Verfahren an einen Optimierungsalgorithmus gekoppelt, um die gewünschten Matrizen zu erzeugen, deren Diagonalelemente identisch 1 sind und für die die nicht als Null vorgegebenen Werte nahe 1 bzw. -1 liegen. Nicht jeder Graph ist prämoralisierbar. Da diese Eigenschaft notwendig ist für das Verfahren zur Erzeugung der Matrizen mit Nebenbedingungen, wird eine empirische Studie durchgeführt, die zeigt, dass ein Großteil der aus Microarray-Daten geschätzten Graphen auch prämoralisierbar ist. Die Arbeit schließt mit praktischen Anwendungen. Die Validierung eines bekannten Algorithmus zum Schätzen von Netzwerken wird durchgeführt und es wird ein Ansatz vorgestellt, mit dem man graphische Strukturen, die aus Microarray-Daten geschätzt worden sind, vergleichen kann, um signifikante Unterschiede zu finden

Differential expression of apoptotic genes PDIA3 and MAP3K5 distinguishes between low- and high-risk prostate cancer

<p>Abstract</p> <p>Background</p> <p>Despite recent progress in the identification of genetic and molecular alterations in prostate cancer, markers associated with tumor progression are scarce. Therefore precise diagnosis of patients and prognosis of the disease remain difficult. This study investigated novel molecular markers discriminating between low and highly aggressive types of prostate cancer.</p> <p>Results</p> <p>Using 52 microdissected cell populations of low- and high-risk prostate tumors, we identified via global cDNA microarrays analysis almost 1200 genes being differentially expressed among these groups. These genes were analyzed by statistical, pathway and gene enrichment methods. Twenty selected candidate genes were verified by quantitative real time PCR and immunohistochemistry. In concordance with the mRNA levels, two genes <it>MAP3K5 </it>and <it>PDIA3 </it>exposed differential protein expression. Functional characterization of <it>PDIA3 </it>revealed a pro-apoptotic role of this gene in PC3 prostate cancer cells.</p> <p>Conclusions</p> <p>Our analyses provide deeper insights into the molecular changes occurring during prostate cancer progression. The genes <it>MAP3K5 </it>and <it>PDIA3 </it>are associated with malignant stages of prostate cancer and therefore provide novel potential biomarkers.</p

Classification across gene expression microarray studies

<p>Abstract</p> <p>Background</p> <p>The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.</p> <p>Results</p> <p>For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In particular, the better predictive results of DV in across platform classification indicate higher robustness of the classifier when trained on single channel data and applied to gene expression ratios.</p> <p>Conclusions</p> <p>We present a systematic evaluation of strategies for the integration of independent microarray studies in a classification task. Our findings in across studies classification may guide further research aiming on the construction of more robust and reliable methods for stratification and diagnosis in clinical practice.</p

Genome-wide gene expression profiling suggests distinct radiation susceptibilities in sporadic and post-Chernobyl papillary thyroid cancers

Papillary thyroid cancers (PTCs) incidence dramatically increased in the vicinity of Chernobyl. The cancer-initiating role of radiation elsewhere is debated. Therefore, we searched for a signature distinguishing radio-induced from sporadic cancers. Using microarrays, we compared the expression profiles of PTCs from the Chernobyl Tissue Bank (CTB, n=12) and from French patients with no history of exposure to ionising radiations (n=14). We also compared the transcriptional responses of human lymphocytes to the presumed aetiological agents initiating these tumours, γ-radiation and H2O2. On a global scale, the transcriptomes of CTB and French tumours are indistinguishable, and the transcriptional responses to γ-radiation and H2O2 are similar. On a finer scale, a 118 genes signature discriminated the γ-radiation and H2O2 responses. This signature could be used to classify the tumours as CTB or French with an error of 15–27%. Similar results were obtained with an independent signature of 13 genes involved in homologous recombination. Although sporadic and radio-induced PTCs represent the same disease, they are distinguishable with molecular signatures reflecting specific responses to γ-radiation and H2O2. These signatures in PTCs could reflect the susceptibility profiles of the patients, suggesting the feasibility of a radiation susceptibility test

Cold atoms in space: community workshop summary and proposed road-map

We summarise the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. Prospective applications include metrology, geodesy and measurement of terrestrial mass change due to, e.g., climate change, and fundamental science experiments such as tests of the equivalence principle, searches for dark matter, measurements of gravitational waves and tests of quantum mechanics. We review the current status of cold atom technologies and outline the requirements for their space qualification, including the development paths and the corresponding technical milestones, and identifying possible pathfinder missions to pave the way for missions to exploit the full potential of cold atoms in space. Finally, we present a first draft of a possible road-map for achieving these goals, that we propose for discussion by the interested cold atom, Earth Observation, fundamental physics and other prospective scientific user communities, together with the European Space Agency (ESA) and national space and research funding agencies.publishedVersio

Cold atoms in space: community workshop summary and proposed road-map

We summarise the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. Prospective applications include metrology, geodesy and measurement of terrestrial mass change due to, e.g., climate change, and fundamental science experiments such as tests of the equivalence principle, searches for dark matter, measurements of gravitational waves and tests of quantum mechanics. We review the current status of cold atom technologies and outline the requirements for their space qualification, including the development paths and the corresponding technical milestones, and identifying possible pathfinder missions to pave the way for missions to exploit the full potential of cold atoms in space. Finally, we present a first draft of a possible road-map for achieving these goals, that we propose for discussion by the interested cold atom, Earth Observation, fundamental physics and other prospective scientific user communities, together with the European Space Agency (ESA) and national space and research funding agencies

Cold atoms in space: community workshop summary and proposed road-map

We summarise the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. Prospective applications include metrology, geodesy and measurement of terrestrial mass change due to, e.g., climate change, and fundamental science experiments such as tests of the equivalence principle, searches for dark matter, measurements of gravitational waves and tests of quantum mechanics. We review the current status of cold atom technologies and outline the requirements for their space qualification, including the development paths and the corresponding technical milestones, and identifying possible pathfinder missions to pave the way for missions to exploit the full potential of cold atoms in space. Finally, we present a first draft of a possible road-map for achieving these goals, that we propose for discussion by the interested cold atom, Earth Observation, fundamental physics and other prospective scientific user communities, together with the European Space Agency (ESA) and national space and research funding agencies

Traces of trauma – a multivariate pattern analysis of childhood trauma, brain structure and clinical phenotypes

Background: Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods: We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results: We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions: Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research

HOW TO use MCRestimate

Estimation the misclassification error Every classification task starts with data. Here we choose the well known ALL/AML data set from T.Golub [1] available in the package golubEsets. Furthermore, we specify the name of the phenodata column that should be used for the classification.&gt; library(MCRestimate)&gt; library(randomForest)&gt; library(golubEsets)&gt; data(Golub_Train)&gt; class.colum &lt;- &quot;ALL.AML&quot; Cross-validation is an appropriate instrument to estimate the misclassification rate of an algorithm that should be used for classification. Often a variable selection or aggregation is applied to the data set before the main classification procedure is started. But these steps must also be part of the cross-validation. In our example we want to perform a variable selection and only take the genes with the highest variance across all samples. Because we don’t know exactly how many genes we want to have, we give two possible values (250 and 1000). The described methods is implemented in the functions g.red.highest.var. Further preprocessing functions are part of the package MCRestimate and also new functions can be implemented.&gt; Preprocessingfunctions &lt;- c(&quot;varSel.highest.var&quot;)&gt; list.of.poss.parameter &lt;- list(var.numbers=c(250,1000)) To use MCRestimate with a classification procedure a wrapper for this method must be available. The package MCRestimate includes wrapper for the following classificatio