Designing the Australian Cancer Atlas: visualizing geostatistical model uncertainty for multiple audiences.

OBJECTIVE: The Australian Cancer Atlas (ACA) aims to provide small-area estimates of cancer incidence and survival in Australia to help identify and address geographical health disparities. We report on the 21-month user-centered design study to visualize the data, in particular, the visualization of the estimate uncertainty for multiple audiences. MATERIALS AND METHODS: The preliminary phases included a scoping study, literature review, and target audience focus groups. Several methods were used to reach the wide target audience. The design and development stage included digital prototyping in parallel with Bayesian model development. Feedback was sought from multiple workshops, audience focus groups, and regular meetings throughout with an expert external advisory group. RESULTS: The initial scoping identified 4 target audience groups: the general public, researchers, health practitioners, and policy makers. These target groups were consulted throughout the project to ensure the developed model and uncertainty visualizations were effective for communication. In this paper, we detail ACA features and design iterations, including the 3 complementary ways in which uncertainty is communicated: the wave plot, the v-plot, and color transparency. DISCUSSION: We reflect on the methods, design iterations, decision-making process, and document lessons learned for future atlases. CONCLUSION: The ACA has been hugely successful since launching in 2018. It has received over 62 000 individual users from over 100 countries and across all target audiences. It has been replicated in other countries and the second version of the ACA was launched in May 2024. This paper provides rich documentation for future projects.

The Need for Children's Advocacy Centres: Hearing the Voices of Children.

Children and young people (CYP) can be empowered to take on roles as agents of change in their own communities. CYP want to be heard and should be actively involved in the co-production, design and development of services aimed at them to ensure that the resulting services are acceptable and accessible. Little analysis of the framing and discourse of co-production in different contexts has been undertaken.Building on Children's Advocacy Center models from the United States of America (which are held in high esteem by local communities), there is perceived value of such a center in the UK. A service development initiative was designed to work with children from Greater Manchester (UK) to determine the potential for the establishment of a children's advocacy center in the North of England. This report presents the design and outcome of the initiative and contributes to the literature on the co-production of such service development projects with CYP, notably the means of achieving that outcome.Recommendations are made for the piloting of an Advocacy House model in the UK with collaborative efforts between CYP as well as health, education, law enforcement, social care providers, charities and voluntary groups. A community-inclusive partnership, underpinned by the principles of co-production and co-design, is integral to the further development of this pilot.

NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data.

A key benefit of long-read nanopore sequencing technology is the ability to detect modified DNA bases, such as 5-methylcytosine. The lack of R/Bioconductor tools for the effective visualization of nanopore methylation profiles between samples from different experimental groups led us to develop the NanoMethViz R package. Our software can handle methylation output generated from a range of different methylation callers and manages large datasets using a compressed data format. To fully explore the methylation patterns in a dataset, NanoMethViz allows plotting of data at various resolutions. At the sample-level, we use dimensionality reduction to look at the relationships between methylation profiles in an unsupervised way. We visualize methylation profiles of classes of features such as genes or CpG islands by scaling them to relative positions and aggregating their profiles. At the finest resolution, we visualize methylation patterns across individual reads along the genome using the spaghetti plot and heatmaps, allowing users to explore particular genes or genomic regions of interest. In summary, our software makes the handling of methylation signal more convenient, expands upon the visualization options for nanopore data and works seamlessly with existing methylation analysis tools available in the Bioconductor project. Our software is available at https://bioconductor.org/packages/NanoMethViz.

ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in the era of data-rich animal science1.

Statistical graphics, and data visualization, play an essential but under-utilized, role for data analysis in animal science, and also to visually illustrate the concepts, ideas, or outputs of research and in curricula. The recent rise in web technologies and ubiquitous availability of web browsers enables easier sharing of interactive and dynamic graphics. Interactivity and dynamic feedback enhance human-computer interaction and data exploration. Web applications such as decision support systems coupled with multimedia tools synergize with interactive and dynamic graphics. However, the importance of graphics for effectively communicating data, understanding data uncertainty, and the state of the field of interactive and dynamic graphics is underappreciated in animal science. To address this gap, we describe the current state of graphical methodology and technology that might be more broadly adopted. This includes an explanation of a conceptual framework for effective graphics construction. The ideas and technology are illustrated using publicly available animal datasets. We foresee that many new types of big and complex data being generated in precision livestock farming create exciting opportunities for applying interactive and dynamic graphics to improve data analysis and make data-supported decisions.

Exploring Perceptions of Parents on the Use of Emergency Department On-site Primary Care Services for the Treatment of Children With Non-urgent Conditions.

This study was conducted to understand the reasons parents of children with minor conditions attend the Children's Emergency Department (ED), and their views about onsite pediatric same day care (SDC) service as an alternative treatment center. The study was a cross-sectional survey of parents attending an inner-city, district general hospital children's ED, with children aged under 16 years old who were allocated to low triage categories. A convenience sample of 58 parents of 58 children were recruited. All the 58 responses were analyzed. Incomplete questionnaires were not excluded. 47% of attendances were because of minor injury. Most presentations were within 24 hours of the injury or illness. 72% of parents were employed. 91% were registered with a General Practitioner (GP). 29% contacted a GP before the ED visit. The majority of participants who contacted a GP were referred to the ED; others were advised to wait to see if the child's condition improved and to attend the ED if there were any concerns or the child deteriorated in any way. About 50% of those that did not contact GP said the GP surgery was closed and 8% felt the GP could not help. 90% of parents perceived their child's condition as urgent requiring immediate treatment. About 33% of parents said they would be happy for their children to be treated at an onsite SDC center. The study showed limited access to GP services in the community and dissatisfaction with community services and perceived urgency of treatment prompted parents of children with minor conditions to attend the ED. This could mean significant ED attendance by children with minor conditions. The majority of the parents in the study would welcome an onsite pediatric SDC if appropriate to meet their children's care needs. Establishing an onsite SDC may help relieve the ED pressures to attend to more clinically urgent and emergency cases.

bigPint: A Bioconductor visualization package that makes big data pint-sized.

Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconductor.org/packages/release/bioc/html/bigPint.html). Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks.

Examining Short-Term Responses to a Long-Term Problem: RNA-Seq Analyses of Iron Deficiency Chlorosis Tolerant Soybean.

Iron deficiency chlorosis (IDC) is a global crop production problem, significantly impacting yield. However, most IDC studies have focused on model species, not agronomically important crops. Soybean is the second largest crop grown in the United States, yet the calcareous soils across most of the upper U.S. Midwest limit soybean growth and profitability. To understand early soybean iron stress responses, we conducted whole genome expression analyses (RNA-sequencing) of leaf and root tissue from the iron efficient soybean (Glycine max) cultivar Clark, at 30, 60 and 120 min after transfer to iron stress conditions. We identified over 10,000 differentially expressed genes (DEGs), with the number of DEGs increasing over time in leaves, but decreasing over time in roots. To investigate these responses, we clustered our expression data across time to identify suites of genes, their biological functions, and the transcription factors (TFs) that regulate their expression. These analyses reveal the hallmarks of the soybean iron stress response (iron uptake and homeostasis, defense, and DNA replication and methylation) can be detected within 30 min. Furthermore, they suggest root to shoot signaling initiates early iron stress responses representing a novel paradigm for crop stress adaptations.

Visualization methods for differential expression analysis.

BACKGROUND: Despite the availability of many ready-made testing software, reliable detection of differentially expressed genes in RNA-seq data is not a trivial task. Even though the data collection is considered high-throughput, data analysis has intricacies that require careful human attention. Researchers should use modern data analysis techniques that incorporate visual feedback to verify the appropriateness of their models. While some RNA-seq packages provide static visualization tools, their capabilities should be expanded and their meaningfulness should be explicitly demonstrated to users. RESULTS: In this paper, we 1) introduce new interactive RNA-seq visualization tools, 2) compile a collection of examples that demonstrate to biologists why visualization should be an integral component of differential expression analysis. We use public RNA-seq datasets to show that our new visualization tools can detect normalization issues, differential expression designation problems, and common analysis errors. We also show that our new visualization tools can identify genes of interest in ways undetectable with models. Our R package "bigPint" includes the plotting tools introduced in this paper, many of which are unique additions to what is currently available. The "bigPint" website is located at https://lindsayrutter.github.io/bigPint and contains short vignette articles that introduce new users to our package, all written in reproducible code. CONCLUSIONS: We emphasize that interactive graphics should be an indispensable component of modern RNA-seq analysis, which is currently not the case. This paper and its corresponding software aim to persuade 1) users to slightly modify their differential expression analyses by incorporating statistical graphics into their usual analysis pipelines, 2) developers to create additional complex and interactive plotting methods for RNA-seq data, possibly using lessons learned from our open-source codes. We hope our work will serve a small part in upgrading the RNA-seq analysis world into one that more wholistically extracts biological information using both models and visuals.

Transcriptomic responses to diet quality and viral infection in Apis mellifera.

BACKGROUND: Parts of Europe and the United States have witnessed dramatic losses in commercially managed honey bees over the past decade to what is considered an unsustainable extent. The large-scale loss of bees has considerable implications for the agricultural economy because bees are one of the leading pollinators of numerous crops. Bee declines have been associated with several interactive factors. Recent studies suggest nutritional and pathogen stress can interactively contribute to bee physiological declines, but the molecular mechanisms underlying interactive effects remain unknown. In this study, we provide insight into this question by using RNA-sequencing to examine how monofloral diets and Israeli acute paralysis virus inoculation influence gene expression patterns in bees. RESULTS: We found a considerable nutritional response, with almost 2000 transcripts changing with diet quality. The majority of these genes were over-represented for nutrient signaling (insulin resistance) and immune response (Notch signaling and JaK-STAT pathways). In our experimental conditions, the transcriptomic response to viral infection was fairly limited. We only found 43 transcripts to be differentially expressed, some with known immune functions (argonaute-2), transcriptional regulation, and muscle contraction. We created contrasts to explore whether protective mechanisms of good diet were due to direct effects on immune function (resistance) or indirect effects on energy availability (tolerance). A similar number of resistance and tolerance candidate differentially expressed genes were found, suggesting both processes may play significant roles in dietary buffering from pathogen infection. CONCLUSIONS: Through transcriptional contrasts and functional enrichment analysis, we contribute to our understanding of the mechanisms underlying feedbacks between nutrition and disease in bees. We also show that comparing results derived from combined analyses across multiple RNA-seq studies may allow researchers to identify transcriptomic patterns in bees that are concurrently less artificial and less noisy. This work underlines the merits of using data visualization techniques and multiple datasets to interpret RNA-sequencing studies.

plyranges: a grammar of genomic data transformation.

Bioconductor is a widely used R-based platform for genomics, but its host of complex genomic data structures places a cognitive burden on the user. For most tasks, the GRanges object would suffice, but there are gaps in the API that prevent its general use. By recognizing that the GRanges class follows "tidy" data principles, we create a grammar of genomic data transformation, defining verbs for performing actions on and between genomic interval data and providing a way of performing common data analysis tasks through a coherent interface to existing Bioconductor infrastructure. We implement this grammar as a Bioconductor/R package called plyranges.

Congruent biogeographical disjunctions at a continent-wide scale: Quantifying and clarifying the role of biogeographic barriers in the Australian tropics.

AIM: To test whether novel and previously hypothesized biogeogaphic barriers in the Australian Tropics represent significant disjunction points or hard barriers, or both, to the distribution of plants. LOCATION: Australian tropics: Australian Monsoon Tropics and Australian Wet Tropics. METHODS: The presence or absence of 6,861 plant species was scored across 13 putative biogeographic barriers in the Australian Tropics, including two that have not previously been recognised. Randomizations of these data were used to test whether more species showed disjunctions (gaps in distribution) or likely barriers (range limits) at these points than expected by chance. RESULTS: Two novel disjunctions in the Australian Tropics flora are identified in addition to eleven putative barriers previously recognized for animals. Of these, eleven disjunction points (all within the Australian Monsoon Tropics) were found to correspond to range-ending barriers to a significant number of species, while neither of the two disjunctions found within the Australian Wet Tropics limited a significant number of species' ranges. MAIN CONCLUSIONS: Biogeographic barriers present significant distributional limits to native plant species in the Australian Monsoon Tropics but not in the Australian Wet Tropics.

Identification of candidate genes involved in early iron deficiency chlorosis signaling in soybean (Glycine max) roots and leaves.

BACKGROUND: Iron is an essential micronutrient for all living things, required in plants for photosynthesis, respiration and metabolism. A lack of bioavailable iron in soil leads to iron deficiency chlorosis (IDC), causing a reduction in photosynthesis and interveinal yellowing of leaves. Soybeans (Glycine max (L.) Merr.) grown in high pH soils often suffer from IDC, resulting in substantial yield losses. Iron efficient soybean cultivars maintain photosynthesis and have higher yields under IDC-promoting conditions than inefficient cultivars. RESULTS: To capture signaling between roots and leaves and identify genes acting early in the iron efficient cultivar Clark, we conducted a RNA-Seq study at one and six hours after replacing iron sufficient hydroponic media (100 µM iron(III) nitrate nonahydrate) with iron deficient media (50 µM iron(III) nitrate nonahydrate). At one hour of iron stress, few genes were differentially expressed in leaves but many were already changing expression in roots. By six hours, more genes were differentially expressed in the leaves, and a massive shift was observed in the direction of gene expression in both roots and leaves. Further, there was little overlap in differentially expressed genes identified in each tissue and time point. CONCLUSIONS: Genes involved in hormone signaling, regulation of DNA replication and iron uptake utilization are key aspects of the early iron-efficiency response. We observed dynamic gene expression differences between roots and leaves, suggesting the involvement of many transcription factors in eliciting rapid changes in gene expression. In roots, genes involved iron uptake and development of Casparian strips were induced one hour after iron stress. In leaves, genes involved in DNA replication and sugar signaling responded to iron deficiency. The differentially expressed genes (DEGs) and signaling components identified here represent new targets for soybean improvement.

Replication protein A subunit 3 and the iron efficiency response in soybean.

In soybean [Glycine max (L.) Merr.], iron deficiency results in interveinal chlorosis and decreased photosynthetic capacity, leading to stunting and yield loss. In this study, gene expression analyses investigated the role of soybean replication protein A (RPA) subunits during iron stress. Nine RPA homologs were significantly differentially expressed in response to iron stress in the near isogenic lines (NILs) Clark (iron efficient) and Isoclark (iron inefficient). RPA homologs exhibited opposing expression patterns in the two NILs, with RPA expression significantly repressed during iron deficiency in Clark but induced in Isoclark. We used virus induced gene silencing (VIGS) to repress GmRPA3 expression in the iron inefficient line Isoclark and mirror expression in Clark. GmRPA3-silenced plants had improved IDC symptoms and chlorophyll content under iron deficient conditions and also displayed stunted growth regardless of iron availability. RNA-Seq comparing gene expression between GmRPA3-silenced and empty vector plants revealed massive transcriptional reprogramming with differential expression of genes associated with defense, immunity, aging, death, protein modification, protein synthesis, photosynthesis and iron uptake and transport genes. Our findings suggest the iron efficient genotype Clark is able to induce energy controlling pathways, possibly regulated by SnRK1/TOR, to promote nutrient recycling and stress responses in iron deficient conditions.

Gradient-based habitat affinities predict species vulnerability to drought.

Ecological fingerprints of climate change are becoming increasingly evident at broad geographical scales as measured by species range shifts and changes in phenology. However, finer-scale species-level responses to environmental fluctuations may also provide an important bellwether of impending future community responses. Here we examined changes in abundance of butterfly species along a hydrological gradient of six montane meadow habitat types in response to drought. Our data collection began prior to the drought, and we were able to track changes for 11 years, of which eight were considered mild to extreme drought conditions. We separated the species into those that had an affinity for hydric vs. xeric habitats. We suspected that drought would favor species with xeric habitat affinities, but that there could be variations in species-level responses along the hydrological gradient. We also suspected that mesic meadows would be most sensitive to drought conditions. Temporal trajectories were modeled for both species groups (hydric vs. xeric affinity) and individual species. Abundances of species with affinity for xeric habitats increased in virtually all meadow types. Conversely, abundances of species with affinity for hydric habitats decreased, particularly in mesic and xeric meadows. Mesic meadows showed the most striking temporal abundance trajectory: Increasing abundances of species with xeric habitat affinity were offset by decreasing or stable abundances of species with hydric habitat affinity. The one counterintuitive finding was that, in some hydric meadows, species with affinity for hydric habitats increased. In these cases, we suspect that decreasing moisture conditions in hydric meadows actually increased habitat suitability because sites near the limit of moisture extremes for some species became more acceptable. Thus, species responses were relatively predictable based upon habitat affinity and habitat location along the hydrological gradient, and mesic meadows showed the highest potential for changes in community composition. The implications of these results are that longer-term changes due to drought could simplify community composition, resulting in prevalence of species tolerant to drying conditions and a loss of species associated with wetter conditions. We contend that this application of gradient analysis could be valuable in assessing species vulnerability of other taxa and ecosystems.

ggbio: an R package for extending the grammar of graphics for genomic data.

We introduce ggbio, a new methodology to visualize and explore genomics annotations and high-throughput data. The plots provide detailed views of genomic regions, summary views of sequence alignments and splicing patterns, and genome-wide overviews with karyogram, circular and grand linear layouts. The methods leverage the statistical functionality available in R, the grammar of graphics and the data handling capabilities of the Bioconductor project. The plots are specified within a modular framework that enables users to construct plots in a systematic way, and are generated directly from Bioconductor data structures. The ggbio R package is available at http://www.bioconductor.org/packages/2.11/bioc/html/ggbio.html.

PKgraph: an R package for graphically diagnosing population pharmacokinetic models.

Population pharmacokinetic (PopPK) modeling has become increasing important in drug development because it handles unbalanced design, sparse data and the study of individual variation. However, the increased complexity of the model makes it more of a challenge to diagnose the fit. Graphics can play an important and unique role in PopPK model diagnostics. The software described in this paper, PKgraph, provides a graphical user interface for PopPK model diagnosis. It also provides an integrated and comprehensive platform for the analysis of pharmacokinetic data including exploratory data analysis, goodness of model fit, model validation and model comparison. Results from a variety of modeling fitting software, including NONMEM, Monolix, SAS and R, can be used. PKgraph is programmed in R, and uses the R packages lattice, ggplot2 for static graphics, and rggobi for interactive graphics.

Graphical inference for Infovis.

How do we know if what we see is really there? When visualizing data, how do we avoid falling into the trap of apophenia where we see patterns in random noise? Traditionally, infovis has been concerned with discovering new relationships, and statistics with preventing spurious relationships from being reported. We pull these opposing poles closer with two new techniques for rigorous statistical inference of visual discoveries. The "Rorschach" helps the analyst calibrate their understanding of uncertainty and "line-up" provides a protocol for assessing the significance of visual discoveries, protecting against the discovery of spurious structure.

Statistical inference for exploratory data analysis and model diagnostics.

We propose to furnish visual statistical methods with an inferential framework and protocol, modelled on confirmatory statistical testing. In this framework, plots take on the role of test statistics, and human cognition the role of statistical tests. Statistical significance of 'discoveries' is measured by having the human viewer compare the plot of the real dataset with collections of plots of simulated datasets. A simple but rigorous protocol that provides inferential validity is modelled after the 'lineup' popular from criminal legal procedures. Another protocol modelled after the 'Rorschach' inkblot test, well known from (pop-)psychology, will help analysts acclimatize to random variability before being exposed to the plot of the real data. The proposed protocols will be useful for exploratory data analysis, with reference datasets simulated by using a null assumption that structure is absent. The framework is also useful for model diagnostics in which case reference datasets are simulated from the model in question. This latter point follows up on previous proposals. Adopting the protocols will mean an adjustment in working procedures for data analysts, adding more rigour, and teachers might find that incorporating these protocols into the curriculum improves their students' statistical thinking.

BarleyBase--an expression profiling database for plant genomics.

BarleyBase (BB) (www.barleybase.org) is an online database for plant microarrays with integrated tools for data visualization and statistical analysis. BB houses raw and normalized expression data from the two publicly available Affymetrix genome arrays, Barley1 and Arabidopsis ATH1 with plans to include the new Affymetrix 61K wheat, maize, soybean and rice arrays, as they become available. BB contains a broad set of query and display options at all data levels, ranging from experiments to individual hybridizations to probe sets down to individual probes. Users can perform cross-experiment queries on probe sets based on observed expression profiles and/or based on known biological information. Probe set queries are integrated with visualization and analysis tools such as the R statistical toolbox, data filters and a large variety of plot types. Controlled vocabularies for gene and plant ontologies, as well as interconnecting links to physical or genetic map and other genomic data in PlantGDB, Gramene and GrainGenes, allow users to perform EST alignments and gene function prediction using Barley1 exemplar sequences, thus, enhancing cross-species comparison.