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Displaying posts with the tag: ggplot2 [Clear Filter]

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[…]

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[…]

Converting excellent spreadsheets to tidy data

Posted by on October 6th, 2017

Structuring data according to the ‘tidy data‘ standard simplifies data analysis and data visualisation. But understanding the structure of tidy data does not come naturally (in my experience), since it is quite different from the structure of data in spreadsheets or tables. Here, I explain how to convert typical spreadsheet data to tidy data to[…]