Straintables: An application that extracts sequences from genome assemblies and generates dissimilarity matrices

Background and Objectives: The dissimilarity matrix (DM) is an important component of phylogenetic analysis, and many software packages exist to build and show DMs. However, as the common input for this type of software are sequences in FASTA file format, the process of extracting and aligning each set of sequences to produce a big number of matrices can be laborious. Additionally, existing software does not facilitate the comparison of clusters of similarity across several DMs built for the same group of individuals, using different genomic regions. To address our requirements of such a tool, we designed Straintables to extract specific genomic region sequences from a group of intraspecies genomic assemblies, using extracted sequences to build dissimilarity matrices. Methods: A Python module with executable scripts was developed for a study on genetic diversity across strains of Toxoplasma gondii, being a general purpose system for DM calculation and visualization for preliminary phylogenetic studies. For automatic region sequence extraction from genomic assemblies we assembled a system that designs virtual primers using reference sequences located at genomic annotations, then matches those primers on genome files by using regex patterns. Extracted sequences are then aligned using Clustal Omega and compared to generate matrices. Results: Using this software saves the user from manual preparation and alignment of the sequences, a process that can be laborious when a large number of assemblies or regions are involved. The automatic sequence extraction process can be checked against BLAST results using the extracted sequence as queries, where correct results were observed for same-species pools for various organisms. The package also contains a matrix visualization tool focused on cluster visualization, capable of drawing matrices into image files with custom settings, and features methods of reordering matrices to facilitate the comparison of clustering patterns across two or more matrices. Conclusion: Straintables may replace and extend the functionality of existing matrix-oriented phylogenetic software, featuring automatic region extraction from genomic assemblies and enhanced matrix visualization capabilities emphasizing cluster identification. This module is open source, available at GitHub (https://github.com/Gab0/straintables) under a MIT license and also as a PIPY package.

PrefMap: Visualization of fuzzy pairwise preference structures

Fuzzy pairwise preferences are an important model to specify and process expert opinions. A fuzzy pairwise preference matrix contains degrees of preference of each option over each other option. Such degrees of preference are often numerically specified by domain experts. In decision processes it is highly desirable to be able to analyze such preference structures, in order to answer questions like: Which objects are most or least preferred? Are there clusters of options with similar preference? Are the preferences consistent or partially contradictory? An important approach for such analysis is visualization. The goal is to produce good visualizations of preference matrices in order to better understand the expert opinions, to easily identify favorite or less favorite options, to discuss and address inconsistencies, or to reach consensus in group decision processes. Standard methods for visualization of preferences are matrix visualization and chord diagrams, which are not suitable for larger data sets, and which are not able to visualize clusters or inconsistencies. To overcome this drawback we propose PrefMap, a new method for visualizing preference matrices. Experiments with nine artificial and real–world preference data sets indicate that PrefMap yields good visualizations that allow to easily identify favorite and less favorite options, clusters, and inconsistencies, even for large data sets.

Visual Multi-Metric Grouping of Eye-Tracking Data

We present an algorithmic and visual grouping of participants and eye-tracking metrics derived from recorded eye-tracking data. Our method utilizes two well-established visualization concepts. First, parallel coordinates are used to provide an overview of the used metrics, their interactions, and similarities, which helps select suitable metrics that describe characteristics of the eye-tracking data. Furthermore, parallel coordinates plots enable an analyst to test the effects of creating a combination of a subset of metrics resulting in a newly derived eye-tracking metric. Second, a similarity matrix visualization is used to visually represent the affine combination of metrics utilizing an algorithmic grouping of subjects that leads to distinct visual groups of similar behavior. To keep the diagrams of the matrix visualization simple and understandable, we visually encode our eye- tracking data into the cells of a similarity matrix of participants. The algorithmic grouping is performed with a clustering based on the affine combination of metrics, which is also the basis for the similarity value computation of the similarity matrix. To illustrate the usefulness of our visualization, we applied it to an eye-tracking data set involving the reading behavior of metro maps of up to 40 participants. Finally, we discuss limitations and scalability issues of the approach focusing on visual and perceptual issues.

High-dimensional data analysis with subspace comparison using matrix visualization

Due to the intricate relationship between different dimensions of high-dimensional data, subspace analysis is often conducted to decompose dimensions and give prominence to certain subsets of dimensions, i.e. subspaces. Exploring and comparing subspaces are important to reveal the underlying features of subspaces, as well as to portray the characteristics of individual dimensions. To date, most of the existing high-dimensional data exploration and analysis approaches rely on dimensionality reduction algorithms (e.g. principal component analysis and multi-dimensional scaling) to project high-dimensional data, or their subspaces, to two-dimensional space and employ scatterplots for visualization. However, the dimensionality reduction algorithms are sometimes difficult to fine-tune and scatterplots are not effective for comparative visualization, making subspace comparison hard to perform. In this article, we aggregate high-dimensional data or their subspaces by computing pair-wise distances between all data items and showing the distances with matrix visualizations to present the original high-dimensional data or subspaces. Our approach enables effective visual comparisons among subspaces, which allows users to further investigate the characteristics of individual dimensions by studying their behaviors in similar subspaces. Through subspace comparisons, we identify dominant, similar, and conforming dimensions in different subspace contexts of synthetic and real-world high-dimensional data sets. Additionally, we present a prototype that integrates parallel coordinates plot and matrix visualization for high-dimensional data exploration and incremental dimensionality analysis, which also allows users to further validate the dimension characterization results derived from the subspace comparisons.