Workshop

Data Analysis 3: Biomedical sciences - Your data analysis

Introduction

In this workshop you will use the tools you used in the previous workshop (and before!) to analyse the data you have collected. You will provide some key pieces of information for the class data set.

Exercises

Getting started on your own analysis

🎬 Open the RStudio Project you created in the Consolidation exercise from week 2.

🎬 If you have not already done so, save your data files to the project. Are the file names going to be easy for you to work with? Remember that we used the file names to label to rows with their treatment (Media, LPS or ECOLIGreen) and antibody (ISOTYPE or TNFAPC) so if you do not match the names you will need to redesign the code appropriately. It is easier to rename your files!

🎬 Open your R script and begin to analyse your data.

Data for the class dataset.

You need to provide the following information for the class dataset:

  • group_name A name for your group. Take care not to use a name used by others. Take care to use the exactly the same name for each of your rows

  • cell_treatmentOne of MEDIA, LPS or ECOLIGreen

  • antibody One of ISOTYPE or TNFAPC

  • n The number of cells in sample after flowAI cleaning

  • n_live The number of cells in sample after flowAI cleaning and removing dead cells/debris (“gating”) (i.e., the % live cells)

  • perc_live The % of cells in sample after flowAI cleaning and removing dead cells/debris (“gating”) (i.e., the % live cells)

  • apc_cut threshold for logicle transformed TNFa_APC_Lin. This should be the same for all your samples and indicates the level at which cells are postive for TNF-α positive

  • mean_apc Mean fluorescence intensity of the logicle transformed TNFa_APC_Lin in the TNF-α positive cells

  • n_pos_tnfa Number of TNF-α positive cells

  • perc_pos_tnfa Percent of live cells that are TNF-α positive

Enter these in the BIO00066I Biomedical Sciences class data

Some columns are required for the class analysis. Other columns help you get to the required values and help others determine the reliability of your data.

How to change the name of an RStudio project.

You will need to rename your RStudio Project to your exam number before you submit. You can change the name of an RStudio project by:

  • closing the project

  • renaming both the project folder and the .Rproj file.

Independent study following the workshop

Consolidate

The Code file

This contains all the code needed in the workshop even where it is not visible on the webpage.

The workshop.qmd file is the file I use to compile the practical. Qmd stands for Quarto markdown. It allows code and ordinary text to be interweaved to produce well-formatted reports including webpages. View the Qmd in Browser. Coding and thinking answers are marked with #---CODING ANSWER--- and #---THINKING ANSWER---

Pages made with R (R Core Team 2024), Quarto (Allaire et al. 2022), knitr (Xie 2024, 2015, 2014), kableExtra (Zhu 2024)

References

Allaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2022. Quarto. https://doi.org/10.5281/zenodo.5960048.
R Core Team. 2024. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Xie, Yihui. 2014. “Knitr: A Comprehensive Tool for Reproducible Research in R.” In Implementing Reproducible Computational Research, edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng. Chapman; Hall/CRC.
———. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. https://yihui.org/knitr/.
———. 2024. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://yihui.org/knitr/.
Zhu, Hao. 2024. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.