Harvard University - Advanced Bioconductor

Offered byedX

Advanced Bioconductor
at
edX
Overview

Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.

Duration	5 hours
Start from	Start Now
Total fee	Free
Mode of learning	Online
Schedule type	Self paced
Difficulty level	Advanced
Official Website	Explore Free Course
Credential	Certificate

Advanced Bioconductor
at
edX
Highlights

Earn a Certificate of completion from Harvard University on successful course completion
Instructors -
Rafael Irizarry & Michael Love
Effort - 2?4 hours per week
FREE
Add a Verified Certificate for ?3,656

Advanced Bioconductor
at
edX
Course details

Skills you will learn

R Statistics Data visualization Data analysis Data Science

Who should do this course?

This course is designed for those who want to learn about Advanced Bioconductor

More about this course

In this course, you will explore the approaches to visualization of genome-scale data, and lean abouttools to build interactive graphical interfaces to speed discovery and interpretation. Using knitr and rmarkdown as basic authoring tools, the concept of reproducible research is developed, and the concept of an executable document is presented. In this framework reports are linked tightly to the underlying data and code, enhancing reproducibility and extensibility of completed analyses. It uses out-of-memory approaches to the analysis of very large data resources, using relational databases or HDF5 as "back ends" with familiar R interfaces. Multiomic data integration is illustrated using a curated version of The Cancer Genome Atlas. Finally, it will explore cloud-resident resources developed for the Encyclopedia of DNA Elements (the ENCODE project). These address transcription factor binding, ATAC-seq, and RNA-seq with CRISPR interference. Given the diversity in educational background of the students the course is divided into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.