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Online Course

Advanced Bioconductor

Harvard University via edX

(1)
361
  • Provider edX
  • Cost Free Online Course (Audit)
  • Session Self Paced
  • Language English
  • Certificate $49 Certificate Available
  • Effort 2-4 hours a week
  • Duration 5 weeks long
  • Learn more about MOOCs

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Overview

In this course, we begin with approaches to visualization of genome-scale data, and provide tools 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. We study 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, we 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 our students we have divided the series 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.

These courses make up two Professional Certificates and are self-paced:

Data Analysis for Life Sciences:

  • PH525.1x: Statistics and R for the Life Sciences
  • PH525.2x: Introduction to Linear Models and Matrix Algebra
  • PH525.3x: Statistical Inference and Modeling for High-throughput Experiments
  • PH525.4x: High-Dimensional Data Analysis

Genomics Data Analysis:

  • PH525.5x: Introduction to Bioconductor
  • PH525.6x: Case Studies in Functional Genomics
  • PH525.7x: Advanced Bioconductor

This class was supported in part by NIH grant R25GM114818.

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Taught by

Michael Love and Rafael Irizarry

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Review for edX's Advanced Bioconductor Based on 1 reviews

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Brandt P
by Brandt completed this course, spending 3 hours a week on it and found the course difficulty to be medium.
(Note I took these prior to their reorganization/combination. Back then they were 4 one-week courses, of which I took three, so I will review those modules below and assume that the material in the new course is similar). >>>>RNA-seq: This is the first case study course and 5th overall course in the...
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