the community site for and by developmental biologists

About: Joachimg

I am a chemist from training, with a strong interest in biology. Our lab develops genetically encoded fluorescent probes and biosensors for quantitative functional imaging with the overarching goal to unravel (G-protein) signalling networks in time and space in cells and tissues. You can follow me on twitter: @joachimgoedhart

Posts by Joachimg:

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[…]

User-friendly p-values

Posted by on February 13th, 2019

A good statistic is the one that you can understand. Mean values are understandable and everybody knows how to calculate them. Most people also realize how the mean value can be skewed by an outlier. So we know what the mean represents and we are aware of its limitations. In sharp contrast, the Null Hypothesis[…]

Experimenting with non-anonymous peer review

Posted by on February 3rd, 2019

Last year, I started to experiment with signing my reports for peer review of manuscripts, inspired by other people on twitter (@kaymtye, @AndrewPlested who in turn were inspired by Leslie Voshall). This year, the experiment is a bit different. I will only review for journals that allow non-anonymous peer-review. Why? That was the question raised[…]

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[…]

Visualizing data with R/ggplot2 – It’s about time

Posted by on May 31st, 2018

The visualization of temporal data by line graphs has been documented and popularized by William Playfair in the 18th century (Aigner et al, 2011; Beniger and Robyn, 1978). Today, time-dependent changes are still depicted by line graphs and ideally accompanied by a measure of uncertainty (Marx, 2013). Below, I provide a ‘walk-through’ for generating such a[…]