Transcriptomics 2 is a continuation of the Transcriptomics 1, and it focuses on finding differences in gene expression. We will start with differential gene expression and continue to look at typical challenges that can be resolved using other methods. We will look at t-test, then use DESeq2 to run a differential gene expression pipeline. Next, we will use Factor Regression Analysis. As a result, you will learn to detect obvious differences between pre-set groups as well as expand that idea to more subtle differences represented by factors that might interact with each other.
We will continue to use the PDX cancer project that was started in Course 1.
Major Trends and Applications
Next Generation Sequencing (NGS) and machine learning are key technologies enabling an unprecedented level of power, precision, and efficacy in understanding genomic regulation. Utilizing these tools will provide a more robust understanding of genetic diseases, expression profiles. The application of NGS data has applications outside of medicine as well, such as in agrotechnology. NGS utilized in Agrotech can help provide more information about insects and climate change.
Course objectives and methodology
The goal of this course is to further expose interested individuals to the field of big-data analysis – biologists and clinicians who currently lack the ability to work with this large scale of data, as well as further develop the analytical skills gained from Transcriptomics 1.
To perform the example analysis detailed in the course, students will need access to the T-BioInfo platform. Account information can be requested in an email to email@example.com. Microsoft Excel is also used for some basic analysis of pipeline results.
We realize that the backgrounds of people interested in this course will be diverse, so we want to provide sufficient background for the hands-on exercises that are included in these course. For the exercises, we will be using the T-BioInfo platform that hides the complexity of code and algorithms behind an intuitive visual interface. This part of the course will be dedicated to a detailed description of the analysis steps that are combined into the pipelines on the platform. As a result, participants will gain independence and will be able to analyze their own data.
Our courses are designed to be accessible to diverse audiences, so we are limiting the number of technical concepts that some might find useful. Additional technical terminology and expanded review can be found on other websites that we will provide links to for additional reading.
We would like to thank the following people who helped to prepare the projects, develop the tools, and compile the the learning resources for this course:
Dr. Leonid Brodsky and the Tauber Bioinformatics Research Center, Dr. Claudia Copeland, Dr. Ron Ferrucci, Dr. Jo Calihan, Julia Panov, Jaclyn Williams
- Lectures 14
- Quizzes 2
- Duration 6 hours
- Skill level All levels
- Language English
- Students 4743
- Certificate Yes
- Assessments Yes
1 Introduction to Transcriptomics 2
2 Statistical Significance of Different Conditions
3 Differential Expression Analysis
4 Factor Regression Analysis
5 Technical Details, Conclusion and Summary
Wonderful course and Helpful Team
This course is the best introduction to differential gene expression. The course provides hands-on practice, introduces different methods and their application. The course was detailed yet easy to understand for a beginner like me. When in doubt the team was very responsive.
This course is interesting and helps understand concepts like Differential Expression and Factor Regression Analysis,the server platform is a great help too to understand the curriculum well explained here.
Transcriptomics 2 is the extension of transcriptomics 1, it takes us much deeper into the sphere of data analysis. This course exposes us to the concept of Differential Expression, Factor regression analysis and many more. The information is well documented by using a very simple vocabulary which is very easy to grasp. I highly recommend this course to everyone.
I loved how it has taken from basics to an intermediate level of understanding and learning machine language, analysing Big data, how one has to focus on very fine elements to run through accuracy in analysis. I loved the quizzes though, very exciting to finish in time. I recommend to take this course with T1 as well.
You simply can't miss this one!
T2 is a very essential course as it bridges the knowledge of biology to data and analytics. This course introduces us to the concept of Differential Expression and the algorithm DESeq2. A very important concept of Confounding and Batch Effect is dealt with here. We learn about Factorial Regression Analysis. The best thing is we get to work with real-world data ( here PDX cancer data-set on breast cancer bio-markers).