48 research outputs found
On Regulatory and Organizational Constraints in Visualization Design and Evaluation
Problem-based visualization research provides explicit guidance toward
identifying and designing for the needs of users, but absent is more concrete
guidance toward factors external to a user's needs that also have implications
for visualization design and evaluation. This lack of more explicit guidance
can leave visualization researchers and practitioners vulnerable to unforeseen
constraints beyond the user's needs that can affect the validity of
evaluations, or even lead to the premature termination of a project. Here we
explore two types of external constraints in depth, regulatory and
organizational constraints, and describe how these constraints impact
visualization design and evaluation. By borrowing from techniques in software
development, project management, and visualization research we recommend
strategies for identifying, mitigating, and evaluating these external
constraints through a design study methodology. Finally, we present an
application of those recommendations in a healthcare case study. We argue that
by explicitly incorporating external constraints into visualization design and
evaluation, researchers and practitioners can improve the utility and validity
of their visualization solution and improve the likelihood of successful
collaborations with industries where external constraints are more present.Comment: 9 pages, 2 figures, presented at BELIV workshop associated with IEEE
VIS 201
Toward a Scalable Census of Dashboard Designs in the Wild: A Case Study with Tableau Public
Dashboards remain ubiquitous artifacts for presenting or reasoning with data
across different domains. Yet, there has been little work that provides a
quantifiable, systematic, and descriptive overview of dashboard designs at
scale. We propose a schematic representation of dashboard designs as node-link
graphs to better understand their spatial and interactive structures. We apply
our approach to a dataset of 25,620 dashboards curated from Tableau Public to
provide a descriptive overview of the core building blocks of dashboards in the
wild and derive common dashboard design patterns. To guide future research, we
make our dashboard corpus publicly available and discuss its application toward
the development of dashboard design tools.Comment: *J. Purich and A. Srinivasan contributed equally to the wor
RekomGNN: Visualizing, Contextualizing and Evaluating Graph Neural Networks Recommendations
Content recommendation tasks increasingly use Graph Neural Networks, but it
remains challenging for machine learning experts to assess the quality of their
outputs. Visualization systems for GNNs that could support this interrogation
are few. Moreover, those that do exist focus primarily on exposing GNN
architectures for tuning and prediction tasks and do not address the challenges
of recommendation tasks. We developed RekomGNN, a visual analytics system that
supports ML experts in exploring GNN recommendations across several dimensions
and making annotations about their quality. RekomGNN straddles the design space
between Neural Network and recommender system visualization to arrive at a set
of encoding and interaction choices for recommendation tasks. We found that
RekomGNN helps experts make qualitative assessments of the GNN's results, which
they can use for model refinement. Overall, our contributions and findings add
to the growing understanding of visualizing GNNs for increasingly complex
tasks
Declaring a tuberculosis outbreak over with genomic epidemiology
We report an updated method for inferring the time at which an infectious disease was transmitted between persons from a
time-labelled pathogen genome phylogeny. We applied the method to 48 Mycobacterium tuberculosis genomes as part of a
real-time public health outbreak investigation, demonstrating that although active tuberculosis (TB) cases were diagnosed
through 2013, no transmission events took place beyond mid-2012. Subsequent cases were the result of progression from
latent TB infection to active disease, and not recent transmission. This evolutionary genomic approach was used to declare the
outbreak over in January 2015
Genomic āDark Matterā in Prostate Cancer: Exploring the Clinical Utility of ncRNA as Biomarkers
Prostate cancer is the most diagnosed cancer among men in the United States. While the majority of patients who undergo surgery (prostatectomy) will essentially be cured, about 30ā40% men remain at risk for disease progression and recurrence. Currently, patients are deemed at risk by evaluation of clinical factors, but these do not resolve whether adjuvant therapy will significantly attenuate or delay disease progression for a patient at risk. Numerous efforts using mRNA-based biomarkers have been described for this purpose, but none have successfully reached widespread clinical practice in helping to make an adjuvant therapy decision. Here, we assess the utility of non-coding RNAs as biomarkers for prostate cancer recurrence based on high-resolution oligonucleotide microarray analysis of surgical tissue specimens from normal adjacent prostate, primary tumors, and metastases. We identify differentially expressed non-coding RNAs that distinguish between the different prostate tissue types and show that these non-coding RNAs can predict clinical outcomes in primary tumors. Together, these results suggest that non-coding RNAs are emerging from the ādark matterā of the genome as a new source of biomarkers for characterizing disease recurrence and progression. While this study shows that non-coding RNA biomarkers can be highly informative, future studies will be needed to further characterize the specific roles of these non-coding RNA biomarkers in the development of aggressive disease
Declaring a tuberculosis outbreak over with genomic epidemiology
We report an updated method for inferring the time at which an infectious disease was transmitted between persons from a time-labelled pathogen genome phylogeny. We applied the method to 48 Mycobacterium tuberculosis genomes as part of a real-time public health outbreak investigation, demonstrating that although active tuberculosis (TB) cases were diagnosed through 2013, no transmission events took place beyond mid-2012. Subsequent cases were the result of progression from latent TB infection to active disease, and not recent transmission. This evolutionary genomic approach was used to declare the outbreak over in January 2015
SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors
Motivation: Next-generation sequencing (NGS) has enabled whole genome and transcriptome single nucleotide variant (SNV) discovery in cancer. NGS produces millions of short sequence reads that, once aligned to a reference genome sequence, can be interpreted for the presence of SNVs. Although tools exist for SNV discovery from NGS data, none are specifically suited to work with data from tumors, where altered ploidy and tumor cellularity impact the statistical expectations of SNV discovery
Research Article Genomic Analysis of a Serotype 5 Streptococcus pneumoniae Outbreak in British Columbia
Background. Streptococcus pneumoniae can cause a wide spectrum of disease, including invasive pneumococcal disease (IPD). From 2005 to 2009 an outbreak of IPD occurred in Western Canada, caused by a S. pneumoniae strain with multilocus sequence type (MLST) 289 and serotype 5. We sought to investigate the incidence of IPD due to this S. pneumoniae strain and to characterize the outbreak in British Columbia using whole-genome sequencing. Methods. IPD was defined according to Public Health Agency of Canada guidelines. Two isolates representing the beginning and end of the outbreak were whole-genome sequenced. The sequences were analyzed for single nucleotide variants (SNVs) and putative genomic islands. Results. The peak of the outbreak in British Columbia was in 2006, when 57% of invasive S. pneumoniae isolates were serotype 5. Comparison of two whole-genome sequenced strains showed only 10 SNVs between them. A 15.5 kb genomic island was identified in outbreak strains, allowing the design of a PCR assay to track the spread of the outbreak strain. Discussion. We show that the serotype 5 MLST 289 strain contains a distinguishing genomic island, which remained genetically consistent over time. Whole-genome sequencing holds great promise for real-time characterization of outbreaks in the future and may allow responses tailored to characteristics identified in the genome
Mutation discovery in regions of segmental cancer genome amplifications from next generation sequencing of tumours
Next generation sequencing has now enabled a cost-effective enumeration of the full mutational complement of a tumour genome - in particular single nucleotide variants (SNVs). However, most current computational and statistical models for analyzing next generation sequencing data do not account for cancer-specific biological properties, including somatic segmental copy number alterations (CNAs), which require special treatment of the data.
Here we present CoNAn-SNV (Copy Number Annotated āSNV): a novel algorithm for the inference of single nucleotide variants (SNVs) that overlap copy number alterations. The method is based on modelling the notion that genomic regions of segmental duplication and amplification induce an extended āgenotype spaceā where a subset of genotypes will exhibit heavily skewed allelic distributions in SNVs (and therefore render them undetectable by methods that assume diploidy). CoNAn-SNV introduces the concept of modelling allelic counts from sequencing data using a panel of Binomial mixture models where the number of mixtures for a given locus in the genome is informed by a discrete copy number state given as input. We applied CoNAn-SNV to a previously published whole genome shotgun data set obtained from a lobular breast cancer and show that it is able to detect 24 experimentally revalidated somatic non-synonymous mutations that were not detected using copy number insensitive SNV discovery algorithms. Importantly, ROC analysis shows that the increased sensitivity of CoNAn-SNV does not result in disproportionate loss of specificity. Our results indicate that in genomically unstable tumours, copy number annotation for SNV detection will be critical to fully characterize the mutational landscape of cancer genomes.
The Binomial mixture model framework, however, is unable to fully cope with the complexity of tumour sequence data. We explore substituting the Binomial mixture model framework with the Beta-Binomial framework to overcome this limitation. The effectiveness of this substitution is compared against the lobular breast carcinoma and the 30 exon capture data sets all derived from triple negative breast cancers. The performance of Binomial and Beta-Binomial mixture model is evaluated against a cohort of exon capture test cases and we report ROC and f-measures.Science, Faculty ofGraduat