Concept: Bar chart
Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.
QIIME (Quantitative Insights Into Microbial Ecology) is one of the most popular open-source bioinformatics suite for performing metagenome, 16S rRNA amplicon and Internal Transcribed Spacer (ITS) data analysis. Although, it is very comprehensive and powerful tool, it lacks a method to provide publication ready taxonomic pie charts. The script plot_taxa_summary.py bundled with QIIME generate a html file and a folder containing taxonomic pie chart and legend as separate images. The images have randomly generated alphanumeric names. Therefore, it is difficult to associate the pie chart with the legend and the corresponding sample identifier. Even if the option to have the legend within the html file is selected while executing plot_taxa_summary.py, it is very tedious to crop a complete image (having both the pie chart and the legend) due to unequal image sizes. It requires a lot of time to manually prepare the pie charts for multiple samples for publication purpose. Moreover, there are chances of error while identifying the pie chart and legend pair due to random alphanumeric names of the images. To bypass all these bottlenecks and make this process efficient, we have developed a python based program, prepare_taxa_charts.py, to automate the renaming, cropping and merging of taxonomic pie chart and corresponding legend image into a single, good quality publication ready image. This program not only augments the functionality of plot_taxa_summary.py but is also very fast in terms of CPU time and user friendly.
- IEEE transactions on visualization and computer graphics
- Published about 2 years ago
We present TimeSpan, an exploratory visualization tool designed to gain a better understanding of the temporal aspects of the stroke treatment process. Working with stroke experts, we seek to provide a tool to help improve outcomes for stroke victims. Time is of critical importance in the treatment of acute ischemic stroke patients. Every minute that the artery stays blocked, an estimated 1.9 million neurons and 12 km of myelinated axons are destroyed. Consequently, there is a critical need for efficiency of stroke treatment processes. Optimizing time to treatment requires a deep understanding of interval times. Stroke health care professionals must analyze the impact of procedures, events, and patient attributes on time-ultimately, to save lives and improve quality of life after stroke. First, we interviewed eight domain experts, and closely collaborated with two of them to inform the design of TimeSpan. We classify the analytical tasks which a visualization tool should support and extract design goals from the interviews and field observations. Based on these tasks and the understanding gained from the collaboration, we designed TimeSpan, a web-based tool for exploring multi-dimensional and temporal stroke data. We describe how TimeSpan incorporates factors from stacked bar graphs, line charts, histograms, and a matrix visualization to create an interactive hybrid view of temporal data. From feedback collected from domain experts in a focus group session, we reflect on the lessons we learned from abstracting the tasks and iteratively designing TimeSpan.
Microfluidics have become an enabling technology for point-of-care and personalized diagnostics. Desirable capabilities of microfluidics-based diagnostic devices include simplicity, portability, low cost and the performance of multiplexed and quantitative measurements, ideally in a high-throughput format. Here we present the multiplexed volumetric bar-chart chip (V-Chip), which integrates all these capabilities in one device. A key feature of the V-Chip is that quantitative results are displayed as bar charts directly on the device-without the need for optical instruments or any data processing or plotting steps. This is achieved by directly linking oxygen production by catalase, which is proportional to the concentration of the analyte, with the displacement of ink along channels on the device. We demonstrate the rapid quantification of protein biomarkers in diverse clinical samples with the V-Chip. The development of the V-Chip thus opens up the possibility of greatly simplified point-of-care and personalized diagnostics.
While bar graphs are designed for categorical data, they are routinely used to present continuous data in studies that have small sample sizes. This presentation is problematic, as many data distributions can lead to the same bar graph and the actual data may suggest different conclusions from the summary statistics. To address this problem, many journals have implemented new policies that require authors to show the data distribution. This paper introduces a free, web-based tool for creating an interactive alternative to the bar graph (http://statistika.mfub.bg.ac.rs/interactive-dotplot/). This tool allows authors with no programming expertise to create customized interactive graphics, including univariate scatterplots, box plots, and violin plots, for comparing values of a continuous variable across different study groups. Individual data points may be overlaid on the graphs. Additional features facilitate visualization of subgroups or clusters of non-independent data. A second tool enables authors to create interactive graphics from data obtained with repeated independent experiments (http://statistika.mfub.bg.ac.rs/interactive-repeated-experiments-dotplot/). These tools are designed to encourage exploration and critical evaluation of the data behind the summary statistics and may be valuable for promoting transparency, reproducibility, and open science in basic biomedical research.
- IEEE transactions on visualization and computer graphics
- Published about 2 months ago
Conventional dot plots use a constant dot size and are typically applied to show the frequency distribution of small data sets. Unfortunately, they are not designed for a high dynamic range of frequencies. We address this problem by introducing nonlinear dot plots. Adopting the idea of nonlinear scaling from logarithmic bar charts, our plots allow for dots of varying size so that columns with a large number of samples are reduced in height. For the construction of these diagrams, we introduce an efficient two-way sweep algorithm that leads to a dense and symmetrical layout. We compensate aliasing artifacts at high dot densities by a specifically designed low-pass filtering method. Examples of nonlinear dot plots are compared to conventional dot plots as well as linear and logarithmic histograms. Finally, we include feedback from an expert review.
An integrated distance-based origami paper analytical device (ID-oPAD) is developed for simple, user-friendly and visual detection of targets of interest. The platform enables complete integration of target recognition, signal amplification and visual signal output, based on aptamer/invertase functionalized sepharose beads, cascaded enzymatic reactions and a 3D microfluidic paper-based analytical device with distance-based readout, respectively. The invertase-DNA conjugate is released upon target addition, after which it permeates through the cellulose and flows down into the bottom detection zone, while sepharose beads with larger size are excluded and stay in the upper zone. Finally, the released conjugate initiates cascaded enzymatic reactions and translates the target signal into a brown bar chart reading. By simply closing the device, the ID-oPAD enables sample-in-answer-out assay within 30 min with visual and quantitative readout. Importantly, bound probe/free probe separation is achieved by taking advantage of the size difference between sepharose beads and cellulose pores, and the downstream enzymatic amplification is realized based on the compatibility of multiple enzymes with corresponding substrates. Overall, with the advantages of low-cost, disposability, simple operation and visual quantitative readout, the ID-oPAD offers an ideal platform for point-of-care testing, especially in resource limited areas.
If many changes are necessary to improve the quality of neuroscience research, one relatively simple step could have great pay-offs: to promote the adoption of detailed graphical methods, combined with robust inferential statistics. Here we illustrate how such methods can lead to a much more detailed understanding of group differences than bar graphs and t-tests on means. To complement the neuroscientist’s toolbox, we present two powerful tools that can help us understand how groups of observations differ: the shift function and the difference asymmetry function. These tools can be combined with detailed visualisations to provide complementary perspectives about the data. We provide implementations in R and Matlab of the graphical tools, and all the examples in the article can be reproduced using R scripts. This article is protected by copyright. All rights reserved.
- IEEE transactions on visualization and computer graphics
- Published 8 months ago
We present a technique for converting a basic D3 chart into a reusable style template. Then, given a new data source we can apply the style template to generate a chart that depicts the new data, but in the style of the template. To construct the style template we first deconstruct the input D3 chart to recover its underlying structure: the data, the marks and the mappings that describe how the marks encode the data. We then rank the perceptual effectiveness of the deconstructed mappings. To apply the resulting style template to a new data source we first obtain importance ranks for each new data field. We then adjust the template mappings to depict the source data by matching the most important data fields to the most perceptually effective mappings. We show how the style templates can be applied to source data in the form of either a data table or another D3 chart. While our implementation focuses on generating templates for basic chart types (e.g. variants of bar charts, line charts, dot plots, scatterplots, etc.), these are the most commonly used chart types today. Users can easily find such basic D3 charts on the Web, turn them into templates, and immediately see how their own data would look in the visual style (e.g. colors, shapes, fonts, etc.) of the templates. We demonstrate the effectiveness of our approach by applying a diverse set of style templates to a variety of source datasets.
In the Surveillance Summaries “Leading Causes of Death in Nonmetropolitan and Metropolitan Areas - United States, 1999-2014” and “Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States,” an error occurred in Figure 5 and Figure 3, respectively. In the last panel of bar charts (stroke), the colors for the left-most set of bars (public health region 1) should be reversed.