Visualizing dimensionality reduction of systems biology data

One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties which become visible in the new coordinate system and which were hard to detect in the original coordinate system. Often however, the application of only one method does not suffice to capture all important signals. Therefore several methods addressing different aspects of the data need to be applied. We have developed a framework for linear and non-linear dimension reduction methods within our visual analytics pipeline SpRay. This includes measures that assist the interpretation of the factorization result. Different visualizations of these measures can be combined with functional annotations that support the interpretation of the results. We show an application to high-resolution time series microarray data in the antibiotic-producing organism Streptomyces coelicolor as well as to microarray data measuring expression of cells with normal karyotype and cells with trisomies of human chromosomes 13 and 21

Real-Time Convolutive Blind Source Separation Based on a Broadband Approach

In this paper we present an efficient real-time implementation of a broadband algorithm for blind source separation (BSS) of convolutive mixtures. A recently introduced matrix formulation allows straightforward simultaneous exploitation of nonwhiteness and nonstationarity of the source signals using second-order statistics. We examine the efficient implementation of the resulting algorithm and introduce a block-on-line update method for the demixing filters. Experimental results for moving speakers in a reverberant room show that the proposed method ensures high separation performance. Our method is implemented on a standard laptop computer and works in realtime

Print processing in Contentus: Restoration of digitized print media

One of the main goals of the Contentus use case was to manage and improve the technical quality of large digital multimedia collections in cultural heritage organizations. Generally, there are two causes for quality impairment of digitized multimedia items: errors during the digitization process and a poor condition of the analog original. While digitization errors may be corrected by re-digitization, any deterioration of analog materials can only be counteracted by digital restoration in post-processing after digitization. This article showcases a unique technique developed in Contentus to restore digitized hectograph archive documents that typically display yellowed paper and faded printing ink. The documents used in this restoration showcase belong to the archive of the Music Information Center the Association of Composers and Musicologists (MIZ) of the former German Democratic Republic (GDR), and were produced between 1960 and 1989. The hectography method was widely adopted in the GDR to copy documents at a large scale. The showcased restoration method enhances the readability of on-screen texts and, as shown by evaluation, lowers the error rate of optical character recognition. In turn, the latter improvement is expected to improve the automated extraction of semantic information entities like persons, places and organizations. The technology presented in this article is an example of how corpora consisting of visually impaired analog media can be prepared for semantic search applications based on automatic content indexing - another major goal of the use case Contentus