While doing the tutorial, you will learn:

1. How to import segmentation data and TIFF images.
2. How to combine datasets from different conditions.
3. How to use ggplot2 to plot comparisons between different conditions.
4. How to do simple statistical tests in R.

In this tutorial, I will show you how you can use BactMAP to help you decide which segmentation program you should use for your microorganism. I ran the analysis in this tutorial for figure S1 and S2 of our preprint, where I compared the segmentation outputs of different programs (and people) for both S. aureus and S. pneumoniae images.

In the tutorial, I'll through the analysis of the S. pneumoniae image. You can do the whole analysis with the S. aureus image (or your own data) instead.

For the S. pneumononiae analysis, I asked my colleagues if they could segment the image below. In this image, the cells are induced with ZnCl₂ to induce the expression of a fluorescent protein. The addition of ZnCl₂ leads to a chainy phenotype which is particularly difficult to segment. Therefore I was curious if there was a specific program performing better at this task than others. I was also curious to see whether our personal preferences and beliefs (when should 2 cells be split?) are influencing the outcome of the segmentation.

Three of my colleagues and myself segmented the image. One of my colleagues even did it twice using two different programs. You can find the settings they used in the .zip folder you downloaded, and read more about the dataset here. Note: this is just a quick, exploratory experiment we did - I hope this can be the start of a proper and more thorough benchmarking!

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1. Before Getting Started
2. Data Import
3. A Closer look
4. Combine all Datasets with combineDataframes
5. Using ggplot2 to compare segmentations_files
6. Using plotRaw to compare segmentations visually
7. Statistical Tests
8. Extra's
9. Resources

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⬅️ Tutorials Overview	Segmentation Tutorial part 1: Before getting Started ➡️