Posted by Joachim Goedhart 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.[…]
Posted by Joachim Goedhart 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[…]
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 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[…]