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Posted by Joachim Goedhart 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[…]
Posted by Joachim Goedhart 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[…]
Posted by Joachim Goedhart 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[…]
Posted by Joachim Goedhart 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[…]
Posted by Joachim Goedhart 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:[…]
Posted by Joachim Goedhart 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[…]
Posted by Joachim Goedhart 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[…]
Posted by Joachim Goedhart on April 18th, 2018
After leaving the bar, what are we to do? I propose to move on to a better bar (and I hope that you will not be disappointed when you find out that I’m actually referring to an interval). In a previous blog I advocated the transparent presentation and reporting of data in graphs. It was[…]
Posted by Joachim Goedhart on April 5th, 2018
In a previous blog, I have disgraced parrots by associating them with P-values and discrediting them for their mechanic repetition. Nevertheless, I admire the vivid colours of these multifaceted birds. Here, I want to make it up by dedicating a pseudo-colour look-up table (LUT) to parrots. The images produced by fluorescence microscopy are best displayed[…]
Posted by Joachim Goedhart on February 1st, 2018
Recently, Nature published my correspondence “Dispense with redundant P values”. It highlights my concern that p-values are often calculated because “everybody does it”. This reminded me of the mechanical repetition that parrots are well-known for (footnote 1). Parroting of p-value reporting should stop and I suggest to only present a p-value in a figure if[…]
