Talks by Uwe Menzel
Wenn die Nullhypothese zutrifft, wenn also ∈ 20, 0.4 , dann hat die Zufallsvariable die im Bild w... more Wenn die Nullhypothese zutrifft, wenn also ∈ 20, 0.4 , dann hat die Zufallsvariable die im Bild wiedergegebene Verteilung. Der beobachtete Wert war = 3. Eine Anzahl von -Werten hat eine geringere (oder gleiche) Wahrscheinlichkeit (rot dargestellt). Die Summe aller dieser Wahrscheinlichkeiten ist der p-Wert.
The Starting Point of the Hormesis Model
Poster by Uwe Menzel
Fin biospies were obtained from 152 individuals of the short-lived teleost fish Nothobranchius fu... more Fin biospies were obtained from 152 individuals of the short-lived teleost fish Nothobranchius furzeri (maximum lifespan ~ 60 weeks), at the age of 10 weeks and 20 weeks. The biopsies were taken without sacrificing the fish, so that lifespan data were available for each individual. Transcriptome data have been generated for these samples using RNA-Seq on the Illumina platform. In order to identify genes which are predictive for lifespan, a Random Forest analysis has been made, considering the expression at both time points as well as the change of the expression between the two time points. Based on this analysis, we can conclude that differences in lifespan manifest in gene expression already at young age. On the other hand, the analysis reveals that batch effects hamper the identification of generally applicable biomarkers for the prediction of lifespan.
A system of non-linear ordinary differential equations (ODE) has been established in order to des... more A system of non-linear ordinary differential equations (ODE) has been established in order to describe the response of a gene regulatory network to environmental signals. It is shown that a double-negative feedback loop in the mTOR pathway can lead to a hormesis-like signal-response characteristic of gene expression levels. Signals with moderate intensity or duration trigger the system to switch to a distinguished stable state that can be related to a putative defense loop. If a certain pulse duration or intensity is exceeded, the defense loop cannot be maintained by the system. The parameters of the model were chosen in such a way that calculated stationary-state values fit to gene expression data measured by RNA-Seq. Using the model, we speculate about processes that possibly contribute to ageing.
Teaching Documents by Uwe Menzel
I would like to update this paper but this does not seem to be possible just now. See www.matstat... more I would like to update this paper but this does not seem to be possible just now. See www.matstat.org instead. U.M.
I would like to update this paper but this does not seem to be possible just now. See www.matstat... more I would like to update this paper but this does not seem to be possible just now. See www.matstat.org instead. U.M.
CRAN by Uwe Menzel
The package RMThreshold attempts to determine an objective threshold which separates signal from ... more The package RMThreshold attempts to determine an objective threshold which separates signal from noise in large real-valued, symmetric matrices. Such matrices can for instance describe correlation or mutual information between data of various origin, or might represent the set of edges in undirected networks. RMThreshold takes advantage of the predictions of Random Matrix Theory (RMT) for the distribution of the spacing between the eigenvalues of such matrices. That distribution is usually called Nearest Neighbor Spacing Distribution (NNSD). The predictions of RMT are valid in the limit of large matrix dimensions. RMT was initiated by Eugene Wigner in the context of nuclear physics in 1955 (Wigner E. P., Annals of Mathematics, 1955). RMT predicts two extreme scenarios for the NNSD of eigenvalues: 1.) If the matrix elements are completely random, the NNSD is characterized by Gaussian Orthogonal Ensemble (GOE) statistics, and the shape of the NNSD resembles the Wigner-Dyson distribution (" Wigner surmise "): , where s is the eigenvalue spacing and P(s) it's distribution. This distribution approaches zero for s = 0 which can be imagined as if there was some sort of " repulsion " between the eigenvalues. 2.) If the matrix has a non-random, modular structure (associated with block-like composition), the NNSD comes close to an Exponential distribution: Both functions differ most at s = 0, where PGOE = 0 and Pexp = 1. An imaginary " repulsion " does not occur in the modular case, and zero-spacings between the eigenvalues frequently occur. This case might apply to the adjacency matrix of a large undirected network consisting of relatively independent clusters with weak connections between them. The connections might possibly just being noise by their nature. By identifying an appropriate threshold for such matrices, it should be possible to reveal the underlying modular structure of the network, i.e. to identify the clusters. Now, if we assume that a matrix or a network actually has a modular structure which is hidden by noise, it should be possible to identify a signal-noise separating threshold by finding the threshold at which the NNSD changes from the Wigner-Dyson case to the Exponential case. Consequentially, the main function of the package (rm.get.threshold) increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the thresholded matrix. A typical procedure to infer a signal-noise separating threshold by using the package RMThreshold may consist of the following steps: 1.) checking the conformity of the input matrix using the function rm.matrix.validation, 2.) running the main function rm.get.threshold in order to find a candidate threshold, 3.) optionally repeat running rm.get.threshold on a smaller interval of thresholds, and 4.) applying the identified threshold to the matrix. The thresholded matrix created by the latter step should then represent the real signal. Some important steps of this procedure are described in more detail in the following text.
Description An algorithm which can be used to determine an objective threshold for signal-noise s... more Description An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network ad-jacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix-or of the corresponding network-can be unraveled.
Description The package provides functions to carry out a Goodness-of-fit test for discrete multi... more Description The package provides functions to carry out a Goodness-of-fit test for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. A p-value can be calculated using different distance measures between observed and expected frequencies. A Monte Carlo method is provided to make the package capable of solving high-dimensional problems.
Papers by Uwe Menzel
Clinical Lung Cancer, Jul 1, 2019
Programmed cell death ligand 1 (PD-L1) expression within the same lung cancer tissue is variable.... more Programmed cell death ligand 1 (PD-L1) expression within the same lung cancer tissue is variable. In this study we evaluated if the PD-L1 expression on small biopsy specimens represent the PD-L1 status of the corresponding resection specimen. Our results indicate a relative good agreement between biopsy and surgical specimens, with a discordance in approximately 10% of the cases. Background: The immunohistochemical analysis of programmed cell death ligand 1 (PD-L1) expression in tumor tissue of nonesmall-cell lung cancer patients has now been integrated in the diagnostic workup. Analysis is commonly done on small tissue biopsy samples representing a minimal fraction of the whole tumor. The aim of the study was to evaluate the correlation of PD-L1 expression on biopsy specimens with corresponding resection specimens. Materials and Methods: In total, 58 consecutive cases with preoperative biopsy and resected tumor specimens were selected. From each resection specimen 2 tumor cores were compiled into a tissue microarray (TMA). Immunohistochemical staining with the antibody SP263 was performed on biopsy specimens, resection specimens (whole sections), as well as on the TMA. Results: The proportion of PDeL1-positive stainings were comparable between the resection specimens (48% and 19%), the biopsies (43% and 17%), and the TMAs (47% and 14%), using cutoffs of 1% and 50%, respectively (P > .39 all comparisons). When the resection specimens were considered as reference, PD-L1 status differed in 16%/5% for biopsies and in 9%/9% for TMAs (1%/50% cutoff). The sensitivity of the biopsy analysis was 79%/82% and the specificity was 90%/98% at the 1%/50% cutoff. The Cohens k value for the agreement between biopsy and tumor. was 0.70 at the 1% cutoff and 0.83 at the 50% cutoff. Conclusion: The results indicate a moderate concordance between the analysis of biopsy and whole tumor tissue, resulting in misclassification of samples in particular when the lower 1% cutoff was used. Clinicians should be aware of this uncertainty when interpreting PD-L1 reports for treatment decisions.
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Talks by Uwe Menzel
Poster by Uwe Menzel
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Papers by Uwe Menzel