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.
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.
Retracted: Resveratrol inhibits oesophageal adenocarcinoma cell proliferation via AMP-activated protein kinase signaling
- Asian Pacific journal of cancer prevention : APJCP
- Published 17 days ago
Retraction: Retracted:Resveratrol inhibits oesophageal adenocarcinoma cell proliferation via AMP-activated protein kinase signaling Asian Pacific Journal of Cancer Prevention (APJCP) has retracted the article titled “Resveratrol Inhibits Oesophageal Adenocarcinoma Cell Proliferation via AMP-activated Protein Kinase Signaling”(1) for reason of having duplicated contents brought to the attention of APJCP’s editorial office by the following email content: “Dear Editors of Gastroenterology and Hepatology, Acta Pharmacologica Sinica, Asian Pac J Cancer Prev, Clinical and Experimental Hypertension, I write to you from the editorial office of PLOS ONE to inform you of concerns related to duplicated content in articles published by your journals. We have been following up on concerns of overlapping text and duplicate Western blots within the following PLOS ONE article:  Berberine Improves Kidney Function in Diabetic Mice via AMPK Activation https://doi.org/10.1371/journal.pone.0113398 Received: June 9, 2014; Accepted: October 23, 2014; Published: November 19, 2014 It was initially brought to our attention that there is duplication of Western blot images between the PLOS ONE article and the following published papers:  Brain Injury (Received 28 Oct 2013, Accepted 4 Jan 2015, Published online 20 Mar 2015) doi: 10.3109/02699052.2015.1004746: Figure 6b GAPDH is similar to Figure 2A AMPK in   Exp Mol Pathol (Received 24 Feb 2014, Accepted 10 Sep 2014, Available online 16 Sep 2014) doi:10.1016/j.yexmp.2014.09.006: Figure 5B GAPDH is similar to Figure 2A AMPK in ; Figure 5C Occludin is similar to Figure 2A LKB1 in   Korean J Physiol Pharmacol, (Received 7 Nov 2013, Accepted 3 Jan 2016) doi: 10.4196/kjpp.2016.20.4.325 RETRACTED: Figure 6B GAPDH is similar to Figure 2A AMPK  Please note that the KJPP paper has been retracted as a result of the content duplication issues. During the course of our follow up, we have discovered additional instances of possible duplication as follows:  Gastroenterology and Hepatology (Accepted 6 Aug 2014, Accepted ms online 28 Aug 2014, Published 23 Feb 2015) doi:10.1111/jgh.12723: Figure 1d AMPK is similar to Figure 2A AMPK in , Figure 3a iNOS is similar to Figure 2A LKB1 in   Acta Pharmacologica Sinica (Received 4 Mar 2014, Accepted 28 July 2014, Published 17 Nov 2014) doi: 10.1038/aps.2014.88: Figure 1 A and B bar charts are similar to Figure 1 A and B bar charts in , Figure 1E AMPK is similar to Figure 2A AMPK in , Figure 1E p-AMPK is similar to Figure 2A P-AMPK in , Figure 1E bar chart is similar to the Figure 2A bar chart in   Asian Pac J Cancer Prev (Published Jan 2014) doi: 10.7314/APJCP.2014.15.2.677: Figure 3A GAPDH is similar to Figure 2A AMPK in  and p27 kip1 is similar to Figure 2A P-AMPK in   Clinical and Experimental Hypertension (Received 15 Sep 2015, Accepted 24 Nov 2015, Published 5 May 2016) doi: 10.3109/10641963.2015.1131288: Figure 2A LKB1 and P-AMPK are similar to Figure 2A LKB1 and P-AMPK in , Figure 2B P-AMPK and AMPK are similar to Figure 2B P-AMPK and AMPK in , Figure 2A and B bar charts appear similar in both articles. Figures 1A, B and C including Western blots and charts appear similar in both articles. Figure 6C AMPK is similar to Figure 2A AMPK in  (note that authors Min Hu and Bo Liu may be the same as authors on the retracted KJPP paper above) Articles , , , and  contain various amounts of duplicated text in the Results sections when compared to the PLOS ONE article. Note that there may be other instances of duplicated data and/or text between the above articles aside from those affecting the PLOS ONE article. For at least some of the duplicated text, it appears that some of the manuscripts were under consideration at overlapping times. We have been informed that an external biotechnology company conducted the Western blot experiments and provided the raw blots to the authors for the PLOS ONE paper. How the duplications in text and figures arose remains unresolved. Although our correspondence with the authors about this matter is ongoing, we have determined that it is appropriate to issue a retraction of the PLOS ONE article, and the retraction notice will provide details of the similarities in content with the above-listed articles. We will also report this matter to the PLOS ONE authors’ institution. I hope that the information provided above is helpful. If you have any questions in the course of any follow up on this matter, please do not hesitate to get in touch. Best wishes, Sarah Bangs PLOS I OPEN FOR DISCOVERY Sarah Bangs, DPhil I Senior Editor PLOS ONE Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom “ Authors did not respond to request for comment. References: Guang-Hua Fan, Zhong-Ming Wang, Xi Yang, Li-Ping Xu, Qin Qin, Chi Zhang, Jian-Xin Ma, Hong-Yan Cheng, Xin-Chen Sun. Resveratrol Inhibits Oesophageal Adenocarcinoma Cell Proliferation via AMP-activated Protein Kinase Signaling. Asian Pac J Cancer Prev, 15 (2), 677-682
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 3 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.
- IEEE transactions on visualization and computer graphics
- Published 10 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.