VizCommender: Computing Text-Based Similarity in Visualization Repositories for Content-Based Recommendations.

Cloud-based visualization services have made visual analytics accessible to a much wider audience than ever before. Systems such as Tableau have started to amass increasingly large repositories of analytical knowledge in the form of interactive visualization workbooks. When shared, these collections can form a visual analytic knowledge base. However, as the size of a collection increases, so does the difficulty in finding relevant information. Content-based recommendation (CBR) systems could help analysts in finding and managing workbooks relevant to their interests. Toward this goal, we focus on text-based content that is representative of the subject matter of visualizations rather than the visual encodings and style. We discuss the challenges associated with creating a CBR based on visualization specifications and explore more concretely how to implement the relevance measures required using Tableau workbook specifications as the source of content data. We also demonstrate what information can be extracted from these visualization specifications and how various natural language processing techniques can be used to compute similarity between workbooks as one way to measure relevance. We report on a crowd-sourced user study to determine if our similarity measure mimics human judgement. Finally, we choose latent Dirichl et al.ocation (LDA) as a specific model and instantiate it in a proof-of-concept recommender tool to demonstrate the basic function of our similarity measure.

SignalLens: Focus+Context applied to electronic time series.

Electronic test and measurement systems are becoming increasingly sophisticated in order to match the increased complexity and ultra-high speed of the devices under test. A key feature in many such instruments is a vastly increased capacity for storage of digital signals. Storage of 10(9) time points or more is now possible. At the same time, the typical screens on such measurement devices are relatively small. Therefore, these instruments can only render an extremely small fraction of the complete signal at any time. SignalLens uses a Focus+Context approach to provide a means of navigating to and inspecting low-level signal details in the context of the entire signal trace. This approach provides a compact visualization suitable for embedding into the small displays typically provided by electronic measurement instruments. We further augment this display with computed tracks which display time-aligned computed properties of the signal. By combining and filtering these computed tracks it is possible to easily and quickly find computationally detected features in the data which are often obscured by the visual compression required to render the large data sets on a small screen. Further, these tracks can be viewed in the context of the entire signal trace as well as visible high-level signal features. Several examples using real-world electronic measurement data are presented, which demonstrate typical use cases and the effectiveness of the design.

VistaClara: an expression browser plug-in for Cytoscape.

SUMMARY: VistaClara is a plug-in for Cytoscape which provides a more flexible means to visualize gene and protein expression within a network context. An extended attribute browser is provided in the form of a graphical and interactive permutation matrix that resembles the heat map displays popular in gene-expression analysis. This extended browser permits a variety of display options and interactions not currently available in Cytoscape. AVAILABILITY: http://chianti.ucsd.edu/cyto_web/plugins/index.php.

Differences in vascular bed disease susceptibility reflect differences in gene expression response to atherogenic stimuli.

Atherosclerosis occurs predominantly in arteries and only rarely in veins. The goal of this study was to test whether differences in the molecular responses of venous and arterial endothelial cells (ECs) to atherosclerotic stimuli might contribute to vascular bed differences in susceptibility to atherosclerosis. We compared gene expression profiles of primary cultured ECs from human saphenous vein (SVEC) and coronary artery (CAEC) exposed to atherogenic stimuli. In addition to identifying differentially expressed genes, we applied statistical analysis of gene ontology and pathway annotation terms to identify signaling differences related to cell type and stimulus. Differential gene expression of untreated venous and arterial endothelial cells yielded 285 genes more highly expressed in untreated SVEC (P<0.005 and fold change >1.5). These genes represented various atherosclerosis-related pathways including responses to proliferation, oxidoreductase activity, antiinflammatory responses, cell growth, and hemostasis functions. Moreover, stimulation with oxidized LDL induced dramatically greater gene expression responses in CAEC compared with SVEC, relating to adhesion, proliferation, and apoptosis pathways. In contrast, interleukin 1beta and tumor necrosis factor alpha activated similar gene expression responses in both CAEC and SVEC. The differences in functional response and gene expression were further validated by an in vitro proliferation assay and in vivo immunostaining of alphabeta-crystallin protein. Our results strongly suggest that different inherent gene expression programs in arterial versus venous endothelial cells contribute to differences in atherosclerotic disease susceptibility.

Cerebral: visualizing multiple experimental conditions on a graph with biological context.

Systems biologists use interaction graphs to model the behavior of biological systems at the molecular level. In an iterative process, such biologists observe the reactions of living cells under various experimental conditions, view the results in the context of the interaction graph, and then propose changes to the graph model. These graphs ser ve as a form of dynamic knowledge representation of the biological system being studied and evolve as new insight is gained from the experimental data. While numerous graph layout and drawing packages are available, these tools did not fully meet the needs of our immunologist collaborators. In this paper, we describe the data information display needs of these immunologists and translate them into design decisions. These decisions led us to create Cerebral, a system that uses a biologically guided graph layout and incorporates experimental data directly into the graph display. Small multiple views of different experimental conditions and a data-driven parallel coordinates view enable correlations between experimental conditions to be analyzed at the same time that the data is viewed in the graph context. This combination of coordinated views allows the biologist to view the data from many different perspectives simultaneously. To illustrate the typical analysis tasks performed, we analyze two datasets using Cerebral. Based on feedback from our collaborators we conclude that Cerebral is a valuable tool for analyzing experimental data in the context of an interaction graph model.

Overview use in multiple visual information resolution interfaces.

In interfaces that provide multiple visual information resolutions (VIR), low-VIR overviews typically sacrifice visual details for display capacity, with the assumption that users can select regions of interest to examine at higher VIRs. Designers can create low-VIRs based on multi-level structure inherent in the data, but have little guidance with single-level data. To better guide design tradeoff between display capacity and visual target perceivability, we looked at overview use in two multiple-VIR interfaces with high-VIR displays either embedded within, or separate from, the overviews. We studied two visual requirements for effective overview and found that participants would reliably use the low-VIR overviews only when the visual targets were simple and had small visual spans. Otherwise, at least 20% chose to use the high-VIR view exclusively. Surprisingly, neither of the multiple-VIR interfaces provided performance benefits when compared to using the high-VIR view alone. However, we did observe benefits in providing side-by-side comparisons for target matching. We conjecture that the high cognitive load of multiple-VIR interface interactions, whether real or perceived, is a more considerable barrier to their effective use than was previously considered.

Pathway analysis of coronary atherosclerosis.

Large-scale gene expression studies provide significant insight into genes differentially regulated in disease processes such as cancer. However, these investigations offer limited understanding of multisystem, multicellular diseases such as atherosclerosis. A systems biology approach that accounts for gene interactions, incorporates nontranscriptionally regulated genes, and integrates prior knowledge offers many advantages. We performed a comprehensive gene level assessment of coronary atherosclerosis using 51 coronary artery segments isolated from the explanted hearts of 22 cardiac transplant patients. After histological grading of vascular segments according to American Heart Association guidelines, isolated RNA was hybridized onto a customized 22-K oligonucleotide microarray, and significance analysis of microarrays and gene ontology analyses were performed to identify significant gene expression profiles. Our studies revealed that loss of differentiated smooth muscle cell gene expression is the primary expression signature of disease progression in atherosclerosis. Furthermore, we provide insight into the severe form of coronary artery disease associated with diabetes, reporting an overabundance of immune and inflammatory signals in diabetics. We present a novel approach to pathway development based on connectivity, determined by language parsing of the published literature, and ranking, determined by the significance of differentially regulated genes in the network. In doing this, we identify highly connected "nexus" genes that are attractive candidates for therapeutic targeting and followup studies. Our use of pathway techniques to study atherosclerosis as an integrated network of gene interactions expands on traditional microarray analysis methods and emphasizes the significant advantages of a systems-based approach to analyzing complex disease.

DNA-based fish species identification protocol.

We have developed a fast, simple, and accurate DNA-based screening method to identify the fish species present in fresh and processed seafood samples. This versatile method employs PCR amplification of genomic DNA extracted from fish samples, followed by restriction fragment length polymorphism (RFLP) analysis to generate fragment patterns that can be resolved on the Agilent 2100 Bioanalyzer and matched to the correct species using RFLP pattern matching software. The fish identification method uses a simple, reliable, spin column- based protocol to isolate DNA from fish samples. The samples are treated with proteinase K to release the nucleic acids into solution. DNA is then isolated by suspending the sample in binding buffer and loading onto a micro- spin cup containing a silica- based fiber matrix. The nucleic acids in the sample bind to the fiber matrix. The immobilized nucleic acids are washed to remove contaminants, and total DNA is recovered in a final volume of 100 mul. The isolated DNA is ready for PCR amplification with the provided primers that bind to sequences found in all fish genomes. The PCR products are then digested with three different restriction enzymes and resolved on the Agilent 2100 Bioanalyzer. The fragment lengths produced in the digestion reactions can be used to determine the species of fish from which the DNA sample was prepared, using the RFLP pattern matching software containing a database of experimentally- derived RFLP patterns from commercially relevant fish species.

PROCAM Study: risk prediction for myocardial infarction using microfluidic high-density lipoprotein (HDL) subfractionation is independent of HDL cholesterol.

BACKGROUND: High-density lipoprotein (HDL) subfractions are among the new emerging risk factors for atherosclerosis. In particular, HDL 2b has been shown to be linked to cardiovascular risk. This study uses a novel microfluidics-based method to establish HDL 2b clinical utility using samples from the Prospective Cardiovascular Muenster (PROCAM) Study. METHODS: Method performance was established by measuring accuracy, precision, linearity and inter-site precision. Serum samples from 503 individuals collected in the context of the PROCAM study were analyzed by electrophoresis on a microfluidics system. Of these, 251 were male survivors of myocardial infarction (cases), while 252 individuals were matched healthy controls. HDL cholesterol, HDL 2b concentration and HDL 2b percentage were analyzed. RESULTS: This novel method showed satisfactory assay performance with an inter-site coefficient of variance of <10% for HDL 2b percentage. Parallel patient testing on 52 samples between two sites resulted in a correlation coefficient of r=0.95. Significant differences were observed in the HDL 2b subfraction between cases and controls independent of other risk factors. Including HDL 2b percentage in logistic regression reduced the number of false positives from 64 to 39 and the number of false negative cases from 48 to 45, in the context of this study. CONCLUSIONS: The novel method showed satisfactory assay performance in addition to drastically reduced analysis times and improved ease of use as compared to other methods. Clinical utility of HDL 2b was demonstrated supporting the findings of previous studies.

An architecture for biological information extraction and representation.

MOTIVATIONS: Technological advances in biomedical research are generating a plethora of heterogeneous data at a high rate. There is a critical need for extraction, integration and management tools for information discovery and synthesis from these heterogeneous data. RESULTS: In this paper, we present a general architecture, called ALFA, for information extraction and representation from diverse biological data. The ALFA architecture consists of: (i) a networked, hierarchical, hyper-graph object model for representing information from heterogeneous data sources in a standardized, structured format; and (ii) a suite of integrated, interactive software tools for information extraction and representation from diverse biological data sources. As part of our research efforts to explore this space, we have currently prototyped the ALFA object model and a set of interactive software tools for searching, filtering, and extracting information from scientific text. In particular, we describe BioFerret, a meta-search tool for searching and filtering relevant information from the web, and ALFA Text Viewer, an interactive tool for user-guided extraction, disambiguation, and representation of information from scientific text. We further demonstrate the potential of our tools in integrating the extracted information with experimental data and diagrammatic biological models via the common underlying ALFA representation. CONTACT: aditya_vailaya@agilent.com.

Comparative genomic hybridization using oligonucleotide microarrays and total genomic DNA.

Array-based comparative genomic hybridization (CGH) measures copy-number variations at multiple loci simultaneously, providing an important tool for studying cancer and developmental disorders and for developing diagnostic and therapeutic targets. Arrays for CGH based on PCR products representing assemblies of BAC or cDNA clones typically require maintenance, propagation, replication, and verification of large clone sets. Furthermore, it is difficult to control the specificity of the hybridization to the complex sequences that are present in each feature of such arrays. To develop a more robust and flexible platform, we created probe-design methods and assay protocols that make oligonucleotide microarrays synthesized in situ by inkjet technology compatible with array-based comparative genomic hybridization applications employing samples of total genomic DNA. Hybridization of a series of cell lines with variable numbers of X chromosomes to arrays designed for CGH measurements gave median ratios for X-chromosome probes within 6% of the theoretical values (0.5 for XY/XX, 1.0 for XX/XX, 1.4 for XXX/XX, 2.1 for XXXX/XX, and 2.6 for XXXXX/XX). Furthermore, these arrays detected and mapped regions of single-copy losses, homozygous deletions, and amplicons of various sizes in different model systems, including diploid cells with a chromosomal breakpoint that has been mapped and sequenced to a precise nucleotide and tumor cell lines with highly variable regions of gains and losses. Our results demonstrate that oligonucleotide arrays designed for CGH provide a robust and precise platform for detecting chromosomal alterations throughout a genome with high sensitivity even when using full-complexity genomic samples.