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How to… Graphical Abstract

Posted by on October 27th, 2020

In my visual communication classes students increasingly want to learn how to make  graphical abstracts. Below I summarized a few key points What are Graphical abstracts? Graphical abstracts are increasingly common to explain biomedical concepts and research results. “Summary slides” have been for long been used in talks or lectures. Today, graphical abstracts are omnipresent[…]

Converting spreadsheets to tidy data – Part 2

Posted by on September 29th, 2020

The superplot was recently proposed as a data visualization strategy that improves the communication of experimental results (Lord et al, 2020). To simplify the visualization of data with a superplot, I created a web tool that is named SuperPlotsOfData (Goedhart, 2020). The superplot tutorial for R , the tutorial for Python and the SuperPlotsOfData web app use the[…]

Crafting plots for movies

Posted by on May 6th, 2020

In a previous blog I explained how animated plots can be made to illustrate the dynamics of data. Animated plots go nicely together with the movies from which the data was extracted. Here, I explain how to display a movie and plot side-by-side, starting from a stack of images and using only open source software.[…]

Dynamic display of data with animated plots

Posted by on April 27th, 2020

Data from time-lapse experiments is often displayed in a graph or plot, to visualize the dynamics of biological systems (Goedhart, 2020). Ironically, the perception of the dynamics is largely lost in a static plot. That’s where animated plots come in. Animated plots are a great way to display the dynamics of the underlying data. Below, I[…]

Data Visualization with Flying Colors

Posted by on August 29th, 2019

The importance of barrier-free use of colors in images and graphs has been highlighted in letters to editors (Miall, 2007), papers (Geissbuehler and Lasser, 2013, Levine, 2009), editorials (anonymous, 2007), columns (Wong, 2011) and on numerous web pages. One of the recommendations is to use a color blindness simulator. Having a color vision deficiency myself,[…]

Data manipulation? It’s normal(ization)!

Posted by on June 25th, 2019

In a previous blog, I have highlighted several ways to visualize the cell-to-cell heterogeneity from time-lapse imaging data. However, I have ignored that data is often rescaled in a way that reduces variability. For time-lapse imaging data, it is common to set the initial fluorescence intensity to 1 (or 100%). As a consequence, any changes[…]

Non-zero baselines: the good, the bad, and the ugly

Posted by on June 20th, 2019

Of all the charts being ridiculed at WTFviz, many get shamed for their lack of a zero-baseline. When teaching DataViz, zero-baselines are invariably a topic of debate. The rules about zero baselines are necessary are often unclear. Therefore, let’s quickly recap. Bar charts: always show zero When we ecnode amounts by length, as done in[…]

Visualizing the heterogeneity of single cell data from time-lapse imaging

Posted by on December 12th, 2018

When we examined the kinetics of Rho GTPase activity in endothelial cells in response to receptor stimulation (Reinhard, 2017), we noticed considerable cell-to-cell heterogeneity. In the original work we published graphs with the average response, reflecting the response of the whole cell population. However, these graphs fail to show the cellular heterogeneity. What is the[…]

Make a difference: the alternative for p-values

Posted by on October 8th, 2018

Calculation and reporting of p-values is common in scientific publications and presentations (Cristea and Ioannidis, 2018). Usually, the p-value is calculated to decide whether two conditions, e.g. control and treatment, are different. Although a p-value can flag differences, it cannot quantify the difference itself (footnote 1). Therefore, p-values fail to answer a very relevant question:[…]

Visualizing data with R/ggplot2 – One more time

Posted by on June 26th, 2018

Experiments are rarely performed in isolation. Usually, several conditions are compared in parallel or sequential experiments. This experimental strategy also applies to time-dependent data, e.g. from timelapse imaging. So, naturally, after I published a ‘walk-through for plotting temporal data using R and ggplot2, I was immediately asked how to plot two (or more) sets of[…]