@article{UrbanRemmeleDittrichetal.2020,
  author    = {Urban, Lara and Remmele, Christian W. and Dittrich, Marcus and Schwarz, Roland F. and M{\"u}ller, Tobias},
  title     = {covRNA: discovering covariate associations in large-scale gene expression data},
  series = {BMC Reserach Notes},
  volume    = {13},
  journal   = {BMC Reserach Notes},
  doi       = {10.1186/s13104-020-04946-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229258},
  year      = {2020},
  abstract  = {Objective The biological interpretation of gene expression measurements is a challenging task. While ordination methods are routinely used to identify clusters of samples or co-expressed genes, these methods do not take sample or gene annotations into account. We aim to provide a tool that allows users of all backgrounds to assess and visualize the intrinsic correlation structure of complex annotated gene expression data and discover the covariates that jointly affect expression patterns. Results The Bioconductor package covRNA provides a convenient and fast interface for testing and visualizing complex relationships between sample and gene covariates mediated by gene expression data in an entirely unsupervised setting. The relationships between sample and gene covariates are tested by statistical permutation tests and visualized by ordination. The methods are inspired by the fourthcorner and RLQ analyses used in ecological research for the analysis of species abundance data, that we modified to make them suitable for the distributional characteristics of both, RNA-Seq read counts and microarray intensities, and to provide a high-performance parallelized implementation for the analysis of large-scale gene expression data on multi-core computational systems. CovRNA provides additional modules for unsupervised gene filtering and plotting functions to ensure a smooth and coherent analysis workflow.},
  language  = {en}
}
@phdthesis{Wawrowsky2007,
  author    = {Wawrowsky, Kolja Alexander},
  title     = {Analysis and Visualization in Multidimensional Microscopy},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-23867},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2007},
  abstract  = {The live sciences currently undergo a paradigm shift to computer aided discoveries. Discoveries in the live sciences were historically made by either direct observation or as a result of chemical assays. Today we see a growing shift toward computer aided analysis and visualization. This gradual process happens in microscopy. Multidimensional laser scanning microscopy can acquire very complex multichannel data from fixed or live specimen. New probes such as visible fluorescent proteins let us observe the expression of genes and track protein localization. Ion sensitive dyes change intensity with the concentration of ions in the cell. The laser scanning confocal allows us to record these processes in three dimensions over time. This work demonstrates the application of software analysis to multidimensional microscopy data. We introduce methods for volume investigation, ion flux analysis and molecular modeling. The visualization methods are based on a multidimensional data model to accommodate complex datasets. The software uses vector processing and multiple processors to accelerate volume rendering and achieve interactive rendering. The algorithms are based on human visual perception and allow the observer a wide range of mixed render modes. The software was used to reconstruct the pituitary development in zebrafish and observe the degeneration of neurons after injury in a mouse model. Calicum indicator dyes have long been used to study calcium fluxes. We optimized the imaging method to minimize impact on the cell. Live cells were imaged continuously for 45 minutes and subjected to increasing does of a drug. We correlated the amplitude of calcium oscillations to increasing doses of a drug and obtain single cell dose response curves. Because this method is very sensitive and measures single cell responses it has potential in drug discovery and characterization. Microtubules form a dynamic cytoskeleton, which is responsible for cell shape, intracellular transport and has an integral role in mitosis. A hallmark of microtubule organization is lateral interactions. Microtubules are bundles by proteins into dense structures. To estimate the contribution of this bundling process, we created a fractal model of microtubule organization. This model demonstrates that morphology of complex microtubule arrays can be explained by bundling alone. In summary we showed that advances in software for visualization, data analysis and modeling lead to new discoveries.},
  subject      = {Konfokale Mikroskopie},
  language  = {en}
}