Computational Gene Expression Analysis with Python

TLDR: Learn to analyze and quantify differences in gene expression using public datasets from the Gene Expression Omnibus. Obtain a detailed understanding of how gene expression analysis works, i.e. what is fold change? See examples of how Python can be used to analyze and visualize gene expression data.

You will learn how to use tools like GEO2R, StringDB, PantherDB, and more to analyze publicly available gene expression data!

The course will guide you on choosing a research topic, finding a dataset, processing the data, and analyzing the data graphically with several tools, like StringDB. As a bonus, you will get insight into how to write a paper about your project.

Example topics for research include:

1. Identifying potential biomarkers for cancer (useful in diagnostics)
2. Analyzing changes in gene expression when a sample is treated with X drug or under Y condition
3. Differences in gene expression between early and late stage cancer (useful in prognosis and drug development)

The example project being done in this course is for identifying blood biomarkers for early stage Parkinson’s disease.

Materials needed:

1. Computer

2. Google Account (for Google Sheets + Colab) or Excel

3. Internet Connection

If there is enough interest, another course will be created that features gene expression analysis with machine learning.