WG Nonparametric Statistics
WG Statistical Bioinformatics
R-package ddepn [Download here]:
Description: DDEPN (Dynamic Deterministic Effects Propagation Networks): Infer signalling networks for timecourse data. Given a matrix of high-throughput genomic or proteomic timecourse data, generated after external perturbation of the biological system, DDEPN models the time-dependent propagation of active and passive states depending on a network structure. Optimal network structures given the experimental data are reconstructed. Two network inference algorithms can be used: inhibMCMC, a Markov Chain Monte Carlo sampling approach and GA, a Genetic Algorithm network optimisation. Inclusion of prior biological knowledge can be done using different network prior models.
Reference: Bender C, Heyde S, Wiemann S, Korf U and Beißbarth T (2011) Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'. BMC Bioinformatics, 12: 291. [abstract]
R-package miRtest [Download here]:
Description: Expression levels of mRNAs are among other factors regulated by microRNAs. A particular microRNA can bind specifically to several target mRNAs and lead to their degradation. Expression levels of both, mRNAs and microRNAs, can be obtained by microarray experiments. In order to increase the power of detecting microRNAs that are differentially expressed between two different groups of samples, "miRtest" incorporates expression levels of their related target gene sets.
R-package pathClass [Download here]:
Description: "pathClass" is a collection of classification methods that use information about feature connectivity in a biological network as an additional source of information. This additional knowledge is incorporated into the classification a priori. Several authors have shown that this approach significantly increases the classification performance.
Reference: Johannes M, Brase JC, Fröhlich H, Gade S, Gehrmann M, Fälth M, Sültmann H and Beißbarth T (2010) Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients. Bioinformatics, 26: 2136-2144. [abstract]
R-package RepeatedHighDim [Download here]:
Description: An important object in the analysis of high-throughput genomic data is to find an association between the expression profile of functional gene sets and the different levels of a group response. Instead of multiple testing procedures which focus on single genes, global tests are usually used to detect a group effect in an entire gene set. "RepeatedHighDim" implements a global test for functional gene sets in two-group designs.
Reference: Jung K, Becker B, Brunner B and Beißbarth T (2011) Comparison of Global Tests for Functional Gene Sets in Two-Group Designs and Selection of Potentially Effect-causing Genes. Bioinformatics, 27: 1377-1383. [abstract]
R-package survGenesInterim [Download here]:
Description: Discovery of biomarkers that are correlated with therapy response and thus with survival is an important goal of medical research on severe diseases, e.g. cancer. Frequently, microarray studies are performed to identify genes of which the expression levels in pretherapeutic tissue samples are correlated to survival times of patients. Typically, such a study can take several years until the full planned sample size is available. Therefore, interim analyses are desirable, offering the possibility of stopping the study earlier, or of performing additional laboratory experiments to validate the role of the detected genes. "survGenesInterim" simulates data and interim analyses with specified parameters and can be employed for the planning of such study.
Reference: Leha A, Beißbarth T and Jung K (2011): Sequential Interim Analyses of Survival Data in DNA Microarray Experiments. BMC Bioinformatics, 12:127. [abstract]
Core Facility Medical Biometry and Statistical Bioinformatics
R-package MediCE [Download here]:
Description:For the treatment of many diseases, medical practitioners can decide between different strategies. Besides effectiveness aspects, as typically studied in clinical trials, the costs of a specific treatment strategy play an important role in health economics. Therefore, practitioners are often confronted with the problem of finding a trade-off between costs and effectiveness. MediCE provides functions for two types of cost-effectiveness analysis. We implemented on the one hand functions that perform Markov Chain Monte Carlo simulations of mulit-state models that represent possible outcomes of medical treatments in order to get the effect and associated costs parameters required for cost-effectiveness analysis. Moreover we provide functions to determine the mean medical costs and calculate the incremental cost-effectiveness ratio of censored time-to-event data.