TICAL - a web-tool for multivariate image clustering and data topology preserving visualization

In life science research bioimaging is often used to study two kinds of features in a sample simultaneously: morphology and co-location of molecular components. While bioimaging technology is rapidly proposing and improving new multidimensional imaging platforms, bioimage informatics has to keep pace in order to develop algorithmic approaches to support biology experts in the complex task of data analysis. One particular problem is the availability and applicability of sophisticated image analysis algorithms via the web so different users can apply the same algorithms to their data (sometimes even to the same data to get the same results) and independently from her/his whereabouts and from the technical features of her/his computer. In this paper we describe TICAL, a visual data mining approach to multivariate microscopy analysis which can be applied fully through the web.We describe the algorithmic approach, the software concept and present results obtained for different example images

A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages

Loyek C, Kölling J, Langenkämper D, Niehaus K, Nattkemper TW. A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages. In: Gama J, Bradley E, Hollmén J, eds. Advances in Intelligent Data Analysis X: 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings. Lecture Notes in Computer Science. Vol 7014. Berlin, Heidelberg: Springer; 2011: 258-269

Spatio-Temporal Metabolite Profiling of the Barley Germination Process by MALDI MS Imaging

Gorzolka K, Kölling J, Nattkemper TW, Niehaus K. Spatio-Temporal Metabolite Profiling of the Barley Germination Process by MALDI MS Imaging. PLOS ONE. 2016;11(3): e0150208.MALDI mass spectrometry imaging was performed to localize metabolites during the first seven days of the barley germination. Up to 100 mass signals were detected of which 85 signals were identified as 48 different metabolites with highly tissue-specific localizations. Oligosaccharides were observed in the endosperm and in parts of the developed embryo. Lipids in the endosperm co-localized in dependency on their fatty acid compositions with changes in the distributions of diacyl phosphatidylcholines during germination. 26 potentially antifungal hordatines were detected in the embryo with tissue-specific localizations of their glycosylated, hydroxylated, and O-methylated derivates. In order to reveal spatio-temporal patterns in local metabolite compositions, multiple MSI data sets from a time series were analyzed in one batch. This requires a new preprocessing strategy to achieve comparability between data sets as well as a new strategy for unsupervised clustering. The resulting spatial segmentation for each time point sample is visualized in an interactive cluster map and enables simultaneous interactive exploration of all time points. Using this new analysis approach and visualization tool germination-dependent developments of metabolite patterns with single MS position accuracy were discovered. This is the first study that presents metabolite profiling of a cereals’ germination process over time by MALDI MSI with the identification of a large number of peaks of agronomically and industrially important compounds such as oligosaccharides, lipids and antifungal agents. Their detailed localization as well as the MS cluster analyses for on-tissue metabolite profile mapping revealed important information for the understanding of the germination process, which is of high scientific interest

Spatio-temporal analysis of metabolite profiles during barley germination

Kölling J, Gorzolka K, Niehaus K, Nattkemper TW. Spatio-temporal analysis of metabolite profiles during barley germination. Presented at the German Conference on Bioinformatics (GCB), Bielefeld, Germany

Detection and visualization of communities in mass spectrometry imaging data.

Wüllems K, Kölling J, Bednarz H, Niehaus K, Hans VH, Nattkemper TW. Detection and visualization of communities in mass spectrometry imaging data. BMC Bioinformatics. 2019;20(1): 303.BACKGROUND: The spatial distribution and colocalization of functionally related metabolites is analysed in order to investigate the spatial (and functional) aspects of molecular networks. We propose to consider community detection for the analysis of m/z-images to group molecules with correlative spatial distribution into communities so they hint at functional networks or pathway activity. To detect communities, we investigate a spectral approach by optimizing the modularity measure. We present an analysis pipeline and an online interactive visualization tool to facilitate explorative analysis of the results. The approach is illustrated with synthetical benchmark data and two real world data sets (barley seed and glioblastoma section).; RESULTS: For the barley sample data set, our approach is able to reproduce the findings of a previous work that identified groups of molecules with distributions that correlate with anatomical structures of the barley seed. The analysis of glioblastoma section data revealed that some molecular compositions are locally focused, indicating the existence of a meaningful separation in at least two areas. This result is in line with the prior histological knowledge. In addition to confirming prior findings, the resulting graph structures revealed new subcommunities of m/z-images (i.e. metabolites) with more detailed distribution patterns. Another result of our work is the development of an interactive webtool called GRINE (Analysis of GRaph mapped Image Data NEtworks).; CONCLUSIONS: The proposed method was successfully applied to identify molecular communities of laterally co-localized molecules. For both application examples, the detected communities showed inherent substructures that could easily be investigated with the proposed visualization tool. This shows the potential of this approach as a complementary addition to pixel clustering methods

WHIDE—a web tool for visual data mining colocation patterns in multivariate bioimages

Motivation: Bioimaging techniques rapidly develop toward higher resolution and dimension. The increase in dimension is achieved by different techniques such as multitag fluorescence imaging, Matrix Assisted Laser Desorption / Ionization (MALDI) imaging or Raman imaging, which record for each pixel an N-dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBIs) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application

Towards Protein Network Analysis using TIS Imaging and Exploratory Data Analysis

Langenkämper D, Kölling J, Khan M, Rajpoot N, Nattkemper TW. Towards Protein Network Analysis using TIS Imaging and Exploratory Data Analysis. Presented at the Workshop on Computational Systems Biology (WCSB), Zürich, Switzerland.Identification, analysis and visualization of functional molecular networks are key objectives in systems biology and the logical extension of existing molecular profiling techniques. Here we used TIS (toponome imaging system) imaging to visualize co-location of proteins in tissue samples, thereby integrating two distinct information domains, morphology and molecular interaction. Using a library of 13 selected dye-conjugated antibodies, TIS recorded a stack of 13 fluorescence images, each showing the same visual field, with high fluorescence values indicating the presence of the corresponding bio-molecule or protein. We show first results obtained using machine learning approaches that allow the identification and spatial analysis of co-location patterns without manual thresholding. The authors believe that TIS imaging in combination with advanced visual data mining methods can contribute substantially to addressing several outstanding issues in systems biology where molecular co-location is involved

Analyzing Multi-Tag Bioimages with BIOIMAX colocation mining tools

Kölling J, Rathke M, Abouna S, Khan M, Nattkemper TW. Analyzing Multi-Tag Bioimages with BIOIMAX colocation mining tools. Presented at the IEEE International Symposium on BIOMEDICAL IMAGING (ISBI), Barcelona.The application of multi-tag protocols in fluorescence microscopy allows the visualization of a large number (> 10) of molecules (i. e. proteins) in a sample (like a tissue section). However, the analysis of such high dimensional bioimages is a difficult task for most of the labs, since software solutions for particular data mining steps are difficult to use or just not available. In this paper we present two new free online tools: MICOLT (Multivariate Image COlocation Tool) and MIFIST (Multivariate Image Frequent Item Set Tool). Both tools can be used via our recently proposed online bioimage analysis platform BioIMAX, so users can upload their bioimage data, apply the tools and share the results with other invited users based on BioIMAX’ concept of shared virtual projects. Data mining with these tools includes the computation and visualization colocation factors well established in the microscopy community (like Mander’s score) and association rule mining following the frequent item set principle, thereby supporting large and small scale analysis

TICAL - a web-tool for multivariate image clustering and data topology preserving visualization

Langenkämper D, Kölling J, Abouna S, Khan M, Niehaus K, Nattkemper TW. TICAL - a web-tool for multivariate image clustering and data topology preserving visualization. Presented at the Microscopic Image Analysis with Applications in Biology (MIAAB), Heidelberg, Germany.In life science research bioimaging is often used to study two kinds of features in a sample simultaneously: morphology and co-location of molecular components. While bioimaging technology is rapidly proposing and improving new multidimensional imaging platforms, bioimage informatics has to keep pace in order to develop algorithmic approaches to support biology experts in the complex task of data analysis. One particular problem is the availability and applicability of sophisticated image analysis algorithms via the web so different users can apply the same algorithms to their data (sometimes even to the same data to get the same results) and independently from her/his whereabouts and from the technical features of her/his computer. In this paper we describe TICAL, a visual data mining approach to multivariate microscopy analysis which can be applied fully through the web.We describe the algorithmic approach, the software concept and present results obtained for different example images