Differential Gene Expression Analysis - Your Complete A to Z
What you'll learn
- You'll be able to apply the knowledge of molecular biology to solve problems in differential gene expression analysis specifically, and bioinfomatics generally
- You'll be able to undertake an end-to-end RNAseq analysis pipeline in R
- You'll be able to do a qPCR analysis in R
- You'll be able to do a pathway analysis
- You'll be able to design bioinformatic experiments and do data interpretation
- You'll get a solid foundation on techniques used in bioinformatics
- You'll learn statistical models and methods used in differential gene expression
- Understanding of basic molecular biology terms such as DNA, RNA, gene and protein is going to be helpful in this course
- Familiarity with R programming and UNIX-like terminal command line is advantageous but not necessary as it'll be covered
- Being open-minded and ready to learn!
Do you want to be a bioinformatician but don't know what it entails? Or perhaps you're struggling with biological data analysis problems? Are you confused amongst the biological, medicals, statistical and analytical terms? Do you want to be an expert in this field and be able to design biological experiments, appropriately apply the concepts and do a complete end-to-end analysis?
This is a comprehensive and all-in-one-place course that will teach you differential gene expression analysis with focus on next-generation sequencing, RNAseq and quantitative PCR (qPCR)
In this course we'll learn together one of the most popular sub-specialities in bioinformatics: differential gene expression analysis. By the end of this course you'll be able to undertake both RNAseq and qPCR based differential gene expression analysis, independently and by yourself, in R programming language. The RNAseq section of the course is the most comprehensive and includes everything you need to have the skills required to take FASTQ library of next-generation sequencing reads and end up with complete differential expression analysis. Although the course focuses on R as a biological analysis environment of choice, you'll also have the opportunity not only to learn about UNIX terminal based TUXEDO pipeline, but also online tools. Moreover you'll become well grounded in the statistical and modelling methods so you can explain and use them effectively to address bioinformatic differential gene expression analysis problems. The course has been made such that you can get a blend of hands-on analysis and experimental design experience - the practical side will allow you to do your analysis, while theoretical side will help you face unexpected problems.
Here is the summary of what will be taught and what you'll be able to do by taking this course:
You'll learn and be able to do a complete end-to-end RNAseq analysis in R and TUXEDO pipelines: starting with FASTQ library through doing alignment, transcriptome assembly, genome annotation, read counting and differential assessment
You'll learn and be able to do a qPCR analysis in R: delta-Ct method, delta-delta-Ct method, experimental design and data interpretation
You'll learn how to apply the knowledge of molecular biology to solve problems in differential gene expression analysis specifically, and bioinformatics generally
You'll learn the technical foundations of qPCR, microarray, sequencing and RNAseq so that you can confidently deal with differential gene expression data by understanding what the numbers mean
You'll learn and be able to use two main modelling methods in R used for differential gene expression: the general linear model as well as non-parametric rank product frameworks
You'll learn about pathway analysis methods and how they can be used for hypothesis generation
You'll learn and be able to visualise gene expression data from your experiments
Who this course is for:
- STEM graduates who don't have a sufficient grasp of molecular biology and want to start a career in bioinformatics
- Anybody who needs a refresher in biological foundations of bioinformatics and differential gene expression analysis
- Students who want to start a higher degree (Bachelor, Masters or PhD) project related to bioinformatics
- People working at pharmaceutical companies or at university and who want to learn about differential gene expression analysis
- Curious learners that want to gauge bioinformatics and differential gene expression analysis
My name is Alexander Abdulkader Kheirallah and I am a data scientist with background in bioinformatics.
After graduating with first-class honours in Applied Biomedical Science as well as PhD in Bioinformatics I worked as a bioinformatician at Cambridge university. My research focussed on functional gene studies and hypotheses generation through the application of 'omics' data. Since then I decided to use the quantitative modelling and programming skills I developed over my career to help solve problems in the wider business community.
As an industrial data scientist I've worked on wide range of predictive projects such as customer churn, customer subscription, customer health, natural language processing, article and web engagement. My stack includes R, python and SQL and I've pushed active and live models to production.
Last but not least, I have an interest in the intersection of Artificial Intelligence and Arts, to explore how to former can inspire the latter.