This beginner friendly R coding course will teach you how to perform accurate and robust immunoassay data analysis.
This course will help you understand the benefits of using R for complex data analysis, how to create reproducible data workflows, and showcase step-by-step analysis examples using real-world biological data.
Dr Toryn Poolman is a lecturer and biostatistician working at University College London. He has extensive experience working with complex biological systems and specialises in applying omics techniques, including RNAseq, phospho-proteomics and microbiome analysis.
This chapter will focus on the basics of the programming language R and how it can be used with Google Colab, a free cloud-based data processing suite.
In this chapter, we will run through real world data examples. All of the files are available below for you to try yourselves. You can download the raw datafiles or use our Github link.
In this chapter, we will learn how to create plots using the ggplot2 package which is include in the pre-installed tidyverse package.
In this chapter, we will use example datasets from different immunoassay applications to show you how to analyse using Google Colab.
The datasets and the jupyter notebooks used are available to you on Github (https://github.com/ProteintechLab/Statistics/tree/main) to help follow along with the analysis.
Congratulations on completing the Proteintech Learning Portal Introduction to R for Life Scientists course.
Let's recap what we have learned.
Logo is adapted from “R logo” by The R Foundation. Used and adapted under CC-BY-SA 4.0 by Proteintech.