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Computational Gene Expression Analysis with Python

Name: Computational Gene Expression Analysis with Python
Availability: InStock
Rating: 4.6 (327 reviews)

4.6(327)7k students

Korshub's take

This course walks through a real bioinformatics workflow: pulling public gene expression datasets from the Gene Expression Omnibus and analyzing them to find meaningful differences between conditions, such as fold change between samples.

What you'll do

Choose a research topic and locate a suitable public dataset
Process and analyze the data using tools like GEO2R, StringDB, and PantherDB
Visualize results with Python
Get a bonus walkthrough on how to write up your findings as a paper

The worked example identifies potential blood biomarkers for early-stage Parkinson's disease, giving you a concrete model to follow for your own project — whether that's cancer biomarker discovery or comparing gene expression under different drug treatments.

Who it's for

Anyone with a computer, internet access, and a Google or Excel account who wants hands-on experience with gene expression analysis, no prior bioinformatics background assumed.

About this course

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:

Identifying potential biomarkers for cancer (useful in diagnostics)
Analyzing changes in gene expression when a sample is treated with X drug or under Y condition
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.